{
  "single-agent": {
    "summary": {
      "method": "single-agent",
      "dataset": "MultiGov-30-v3-claude-sonnet-4-6",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.6565000575516085,
      "avg_latency_ms": 7204.732820000208,
      "avg_cost_usd": 0.06619440000000006,
      "avg_tokens": 868025.4666666667,
      "avg_rounds": 1.0,
      "avg_nodes": 1.0,
      "total_cost_usd": 1.9858320000000016,
      "total_latency_s": 216.14198460000625,
      "total_shacl_pass": 0,
      "total_shacl_fail": 0,
      "total_constraint_propagations": 0,
      "total_observer_redirects": 0,
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    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "## Prohibited AI Practices and Penalties Under the EU AI Act\n\n### Prohibited Practices (Article 5)\n\nThe following AI practices are **completely prohibited**:\n\n1. **Subliminal or manipulative techniques** that materially distort behavior, impairing informed decision-making and causing significant harm\n2. **Exploiting vulnerabilities** of specific groups based on age, disability, or social/economic situation\n3. **Social scoring by public authorities** or on their behalf\n4. **Real-time remote biometric identification** in publicly accessible spaces for law enforcement (with narrow exceptions for abduction victims, imminent threats to life, or genuine terrorist threats)\n5. **Untargeted scraping of facial images** from internet or CCTV for facial recognition databases\n6. **Emotion recognition** in workplaces and educational institutions\n7. **Biometric categorisation** using sensitive characteristics\n\n### Penalties\n\nViolations carry the **highest penalty tier** under the AI Act:\n- **Up to \u20ac35 million or 7% of worldwide annual turnover**, whichever is **higher**\n\n### Key Enforcement Details\n\n- Enforcement began **2",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 7008.47590000194,
        "cost_usd": 0.066348,
        "total_tokens": 21113,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "Based on the provided context, the complete compliance chain a provider must follow is:\n\n1. **Classify** the AI system per Article 6 (check Annex I and Annex III categories)\n\n2. **Establish required systems** for high-risk AI:\n   - Risk management system (Art. 9)\n   - Data governance (Art. 10)\n   - Technical documentation (Art. 11)\n   - Record-keeping (Art. 12)\n   - Transparency/instructions for use (Art. 13)\n   - Human oversight design (Art. 14)\n   - Accuracy, robustness, and cybersecurity measures (Art. 15)\n   - Quality management system (Art. 17)\n\n3. **Undergo conformity assessment** (Art. 43) \u2014 either self-assessment (Annex VI) for most Annex III categories when harmonised standards apply, or third-party notified body assessment (Annex VII) for biometric systems and Annex I products\n\n4. **Draw up EU Declaration of Conformity** (Art. 47)\n\n5. **Register** the system",
        "exact_match": 0.0,
        "f1": 0.6009389671361501,
        "latency_ms": 10221.794900000532,
        "cost_usd": 0.066363,
        "total_tokens": 95493,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "Based on Article 14 and Article 26 of the AI Act, deployers of high-risk AI systems have the following human oversight obligations:\n\n**Assignment of Competent Persons (Art. 26(b)):** Deployers must assign human oversight to **competent natural persons** who have the necessary skills and authority to effectively oversee the AI system.\n\n**Implementation of Provider-Identified Measures (Art. 14):** Deployers must implement human oversight measures identified by the provider as appropriate for the deployer to carry out. These measures must include:\n- Tools to **interpret outputs**\n- Ability to **decide not to use or override** the system\n- Ability to **intervene and halt** the system\n\n**Guarding Against Automation Bias (Art. 14(4)(b)):** Oversight measures must specifically prevent over-reliance on AI outputs, particularly for decisions affecting natural persons. This means the system must present outputs as **recommendations**, not definitive answers, and encourage independent human judgment.\n\n**Monitoring Operations (Art. 26(d)):** Deployers must monitor the AI system's operation in accordance with the instructions for use.\n\n**",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 6466.178200000286,
        "cost_usd": 0.06634499999999999,
        "total_tokens": 155867,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "Based on the provided context, Article 51 and Article 55 impose the following specific requirements on GPAI models with systemic risk:\n\n1. **Model evaluation** using state-of-the-art protocols, including red teaming\n2. **Adversarial testing** to assess and mitigate systemic risks, including risks of misuse for CBRN threats, cyber-attacks, and large-scale disinformation\n3. **Incident tracking and reporting** of serious incidents and possible corrective measures to the AI Office and national authorities\n4. **Cybersecurity protections** \u2014 ensuring adequate cybersecurity measures are in place\n5. **Energy consumption documentation** and reporting\n\nAdditionally, GPAI models with systemic risk must make available **sufficient technical documentation** to downstream providers integrating the model into high-risk AI systems.\n\nCompliance can be demonstrated through **codes of practice** (Article 56), coordinated by the EU AI Office, which create a presumption of compliance when followed. The first codes of practice were due by August 2025.\n\nThe **classification threshold** for systemic risk is a cumulative training computation exceeding **10\u00b2\u2075",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 7423.755799998617,
        "cost_usd": 0.06635400000000002,
        "total_tokens": 218870,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "## GDPR Principles for Personal Data Processing and AI Training\n\n### The Six Core Principles (Article 5 GDPR)\n\nPersonal data must be processed in accordance with:\n\n1. **Lawfulness, fairness, and transparency**\n2. **Purpose limitation** \u2014 collected for specified, explicit, legitimate purposes; not further processed incompatibly\n3. **Data minimisation** \u2014 adequate, relevant, and limited to what is necessary\n4. **Accuracy** \u2014 kept up to date\n5. **Storage limitation** \u2014 kept no longer than necessary\n6. **Integrity and confidentiality** \u2014 appropriate security measures\n\nThe controller must also demonstrate compliance (**accountability**).\n\n---\n\n### Application to AI Training\n\n#### Key Tensions and Resolutions\n\n**Data Minimisation vs. AI Performance**\nThere is a fundamental tension: AI models generally perform better with more data, but GDPR requires data limited to what is necessary. Resolution approaches include:\n- Defining a clear purpose and collecting only what that purpose requires\n- Using **synthetic data** to augment training instead of collecting more personal data\n- Applying **federated learning** to train on distributed data without centralisation",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 5666.94439999992,
        "cost_usd": 0.06633899999999993,
        "total_tokens": 277049,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "Based on Article 50 of the AI Act:\n\n**Providers** of AI systems generating synthetic audio, image, video, or text must ensure outputs are **marked in a machine-readable format** as artificially generated or manipulated (Art. 50(2)).\n\n**Deployers** that generate or manipulate image, audio, or video constituting a deepfake must **disclose that the content has been artificially generated** (Art. 50(4)).\n\n**Exemptions** to deepfake disclosure include:\n- Content used for law enforcement/national security\n- Manifestly artistic or satirical work (parody, caricature)\n- Content constituting part of a media freedom exercise with editorial safeguards\n\nThese obligations apply from **2 August 2025**, with non-compliance fines up to **EUR 7.5 million or 1% of worldwide annual turnover**. Technical implementation approaches include C2PA metadata standards, invisible watermarking (e.g., SynthID), and statistical text watermarking. For GPAI models, watermarking must be implemented at the model level so downstream deployers inherit the capability.",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 6742.39039999884,
        "cost_usd": 0.06632700000000002,
        "total_tokens": 323509,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
        "predicted_answer": "Based on the provided context, DORA requires financial entities to have an **internal governance and control framework** for effective and prudent management of all ICT risks. Specifically:\n\n- The **management body** must define, approve, oversee, and be responsible for implementing all ICT risk management arrangements\n- The framework must include **strategies, policies, procedures, ICT protocols and tools** to protect all information and ICT assets\n- Entities must **minimise ICT risk impact** by deploying appropriate strategies and maintaining updated ICT systems\n- Systems must be appropriate to the **nature, variety, complexity and magnitude** of the entity's operations\n\nThe framework applies to virtually all regulated financial entities (credit institutions, payment institutions, investment firms, insurance undertakings, etc.) and has applied since **17 January 2025**.\n\nFor AI systems specifically, DORA requires financial entities to: identify AI systems as ICT assets, assess business process dependencies, maintain **manual fallback procedures**, subject model updates to change management, and conduct **digital operational resilience testing** including threat-led penetration testing for significant entities.\n\nImportantly, DORA's ICT risk management requirements operate **alongside** \u2014 not instead",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 15602.885599997535,
        "cost_usd": 0.06633899999999993,
        "total_tokens": 379107,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
        "predicted_answer": "Based on the provided context, NIS2 requires essential entities to follow a **three-stage notification process**:\n\n1. **Early warning within 24 hours** of becoming aware of the incident (indicating whether it is suspected to involve unlawful or malicious acts)\n2. **Incident notification within 72 hours** \u2014 updating the early warning with an initial assessment of severity and impact\n3. **Final report within one month** after the incident notification \u2014 including detailed description, root cause analysis, mitigation measures, and cross-border impact where applicable\n\nNotifications are submitted to the entity's **CSIRT or competent authority**, which must provide a response within 24 hours of receiving the early warning.",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 4479.995699999563,
        "cost_usd": 0.06482399999999999,
        "total_tokens": 425156,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
        "predicted_answer": "Based on the provided context, Annex III Category 4 covers **employment, workers management and access to self-employment**, specifically including **recruitment, performance evaluation**.",
        "exact_match": 0.0,
        "f1": 0.1839080459770115,
        "latency_ms": 2724.5574000007764,
        "cost_usd": 0.06308099999999994,
        "total_tokens": 471021,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
        "predicted_answer": "Based on the provided context, AI regulatory sandboxes under the EU AI Act (Articles 57-60) serve the following role and scope:\n\n**Role:** Sandboxes provide a **controlled environment** for developing, testing, and validating innovative AI systems for a limited time before market placement, operating under direct supervision and guidance of competent authorities.\n\n**Key scope elements:**\n- Member States must establish **at least one national sandbox by 2 August 2026**\n- Participants may test high-risk AI systems in real-world conditions under specific safeguards: informed consent of subjects, ability to halt testing at any time, and ongoing monitoring/oversight\n- **Prohibited AI practices (Article 5) remain prohibited** even within sandboxes \u2014 they cannot be used to circumvent core prohibitions\n- Real-world testing (Article 60) requires an approved testing plan, informed consent, necessity limits, and immediate halt capability if safety concerns arise\n\n**Benefits for participants:**\n- Direct regulatory guidance during development\n- Legal certainty on classification and compliance\n- Streamlined conformity assessment for sandbox-developed systems\n- Access to high-quality data under controlled conditions\n- Evidence gathered can support conform",
        "exact_match": 0.0,
        "f1": 0.6666666666666666,
        "latency_ms": 7537.81909999816,
        "cost_usd": 0.06634800000000007,
        "total_tokens": 521473,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B01",
        "question": "How do the EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons?",
        "gold_answer": "GDPR Article 22 gives data subjects the right not to be subject to solely automated decisions with legal effects, with rights to human intervention, express their view, and contest decisions. The AI Act complements this: Article 26(7) requires deployers of high-risk AI systems making decisions about natural persons to inform them of the AI system's use. Article 13 requires sufficient transparency for deployers to interpret outputs. Article 14 mandates human oversight with override capability. Together, these create a layered protection: GDPR provides the right to contest, AI Act ensures transparency and human control over the decision process.",
        "predicted_answer": "## Joint Regulation of Automated Decision-Making Under EU AI Act and GDPR\n\n### The Dual Framework\n\n**GDPR Article 22** grants individuals the right not to be subject to decisions based **solely on automated processing** that produce legal or similarly significant effects. Exceptions apply for contract necessity, legal authorization, or explicit consent \u2014 but even then, the individual retains rights to human intervention, to express their point of view, and to contest the decision.\n\n**AI Act Article 14** requires that high-risk AI systems be designed for **effective human oversight**, specifically to prevent automation bias \u2014 the tendency to over-rely on AI outputs without critical evaluation.\n\n---\n\n### How They Interact\n\nThe two frameworks create a **double mandate** for meaningful human involvement:\n\n- GDPR Art. 22 requires the human reviewer to have **genuine authority and actual discretion** \u2014 not merely rubber-stamp the AI output\n- AI Act Art. 14 requires the system itself to be **designed** to prevent automation bias, including presenting outputs as recommendations rather than definitive answers\n\nTogether, they require both the **system design** (AI Act) and the **decision process** (GDPR) to",
        "exact_match": 0.0,
        "f1": 0.7142857142857143,
        "latency_ms": 6718.584900001588,
        "cost_usd": 0.066357,
        "total_tokens": 567761,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B02",
        "question": "What are the combined penalty exposures when a high-risk AI system violates both the EU AI Act and GDPR?",
        "gold_answer": "Under GDPR Article 83, violations of basic principles or data subject rights face fines up to EUR 20 million or 4% of worldwide annual turnover. Under AI Act Article 99, Chapter III violations (high-risk requirements) face fines up to EUR 15 million or 3% of turnover, while Article 5 prohibited practices face EUR 35 million or 7%. These fines are cumulative \u2014 a single AI system that both violates GDPR data principles in its training data and fails AI Act high-risk requirements could face up to 7% (combined 4%+3%) of global turnover, or up to 11% if it also engages in prohibited practices (7%+4%).",
        "predicted_answer": "Based on the provided context, the combined penalty exposures are:\n\n- **AI Act (high-risk violations):** Up to EUR 15 million or 3% of worldwide annual turnover (Art. 99(4))\n- **GDPR violations:** Up to EUR 20 million or 4% of worldwide annual turnover (Art. 83(5))\n\nThese fines are **cumulative** \u2014 both can apply to the same AI system. AI Act Art. 99(7) requires coordination between authorities to ensure proportionality, but does **not** deduplicate the fines, as they cover different aspects of the same conduct.\n\nFor financial entities, additional layers stack on top:\n- **DORA** penalties for ICT risk management failures\n- **NIS2** (if essential entity): up to EUR 10 million or 2% of turnover\n\nThe theoretical **maximum cumulative exposure** for a prohibited AI practice (rather than high-risk) in a financial essential entity processing personal data could reach approximately **7% (AI Act) + 4% (GDPR) + 2% (NIS2) + DORA penalties",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 6251.0112000018125,
        "cost_usd": 0.06637199999999988,
        "total_tokens": 641561,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B03",
        "question": "How must a deployer using a high-risk AI system comply with both the AI Act's fundamental rights impact assessment and GDPR's data protection impact assessment?",
        "gold_answer": "The AI Act Article 27 requires public bodies and private entities providing public services to perform a Fundamental Rights Impact Assessment (FRIA) before deploying high-risk AI. GDPR Article 35 requires a DPIA when processing is likely to result in high risk to rights and freedoms. Both must identify risks, assess impact, and define mitigation measures. Deployers may combine the FRIA and DPIA to avoid duplication. The AI Act Article 26(9) specifically requires deployers to use the system's transparency documentation (Art. 13) to fulfill their GDPR DPIA obligations, creating a documented chain from provider's transparency documentation through to deployer's impact assessment.",
        "predicted_answer": "## Compliance with FRIA and DPIA Requirements\n\n### Mandatory Triggers\n\nA deployer must conduct a **Fundamental Rights Impact Assessment (FRIA)** under AI Act Art. 26(1a) if they are:\n- A body governed by public law or a private entity providing public services, **or**\n- A deployer of credit scoring AI (Annex III Cat. 5b) or insurance risk assessment AI (Annex III Cat. 5c)\n\nA **DPIA** under GDPR Art. 35 is required when processing is likely to result in high risk, including systematic automated evaluation or large-scale special category data processing.\n\n### Explicit Legal Connection\n\nAI Act Art. 26(g) **explicitly mandates** that deployers use the provider's Art. 13 transparency documentation to carry out the GDPR Art. 35 DPIA \u2014 creating a direct legal pipeline:\n\n> **Provider (Art. 13 documentation) \u2192 Deployer (Art. 26(g) DPIA obligation) \u2192 DPA consultation if residual risk remains high**\n\n### Key Differences Between the Two Assessments",
        "exact_match": 0.0,
        "f1": 0.5714285714285714,
        "latency_ms": 6428.756999997859,
        "cost_usd": 0.06639300000000015,
        "total_tokens": 716736,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B04",
        "question": "What dual obligations apply when a financial entity reports an AI-related incident under both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 73, providers must report serious AI incidents to market surveillance authorities within 15 days of establishing a causal link. Under DORA Articles 17-23, financial entities must report major ICT incidents to the financial supervisory authority using standardised templates, with classification based on clients affected, duration, geographical spread, data losses, service criticality, and economic impact. Both reporting obligations apply simultaneously \u2014 to different authorities with different timelines and different templates. The financial entity must report the same AI incident twice: once to the market surveillance authority (AI Act, 15 days) and once to the financial supervisor (DORA, based on incident classification timeline).",
        "predicted_answer": "Based on the provided context, when a financial entity reports an AI-related incident under both the AI Act and DORA, the following dual obligations apply:\n\n**Different Authorities:**\n- AI Act Art. 73: Report to the **market surveillance authority** within 15 days\n- DORA Art. 19: Report to the **financial competent authority** (e.g., ECB, BaFin) with an initial notification within **4 hours**, intermediate report within 72 hours, and final report within 1 month\n\n**Different Timelines:**\n- DORA has the fastest deadline at **4 hours** for the initial notification\n- AI Act allows up to **15 days** for reporting serious incidents\n\nAdditionally, if personal data is involved (GDPR Art. 33) or if the entity is an essential/important entity under NIS2, up to **four separate reports** must be filed with four different authorities under four different timelines \u2014 creating what the context describes as a \"quadruple reporting scenario.\" Organizations must maintain a **unified incident response process** that triggers all applicable notifications from a single assessment.",
        "exact_match": 0.0,
        "f1": 0.6666666666666666,
        "latency_ms": 11253.243300001486,
        "cost_usd": 0.0662609999999999,
        "total_tokens": 775640,
        "convergence_rounds": 1,
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      },
      {
        "question_id": "MG-B05",
        "question": "How do the AI Act's cybersecurity requirements for high-risk AI interact with NIS2 security measures for essential entities?",
        "gold_answer": "AI Act Article 15 requires high-risk AI systems to be resilient against adversarial attacks including poisoning, model evasion, and confidentiality attacks, with technical measures to prevent, detect, respond to and control such attacks. NIS2 Article 21 requires essential entities to implement: risk analysis policies, incident handling, business continuity, supply chain security, vulnerability handling, cryptography and encryption, multi-factor authentication, and cybersecurity training. When a high-risk AI system is operated by an essential entity (e.g., critical infrastructure), both sets of requirements apply: the AI system must meet AI Act Art. 15 standards AND be integrated into the entity's NIS2 cybersecurity framework (Art. 21).",
        "predicted_answer": "Based on the provided context, the AI Act's cybersecurity requirements (Article 15) and NIS2 security measures (Article 21) must be satisfied **simultaneously** for AI systems deployed by essential entities, creating a dual compliance obligation.\n\n**AI Act Article 15** requires high-risk AI systems to be resilient against adversarial attacks including data poisoning, model evasion, model inversion, and model theft, with specific technical measures such as input validation, adversarial training, output monitoring, access controls, and audit logging.\n\n**NIS2 Article 21** requires essential entities to implement cybersecurity risk management measures including risk analysis policies, incident handling, supply chain security, vulnerability handling, encryption, and multi-factor authentication.\n\nFor AI systems in critical infrastructure (Annex III Category 2), both sets of requirements apply concurrently. Specifically, supply chain security must satisfy **both** AI Act Article 15(4) (adversarial resilience) **and** NIS2 Article 21(2)(d) (supply chain security). Additionally, the national competent authority for NIS2 may differ from the AI Act market surveillance authority, meaning organizations must",
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      },
      {
        "question_id": "MG-B06",
        "question": "How does the GDPR's data protection by design principle interact with the AI Act's data governance requirements?",
        "gold_answer": "GDPR Article 25 requires data protection by design \u2014 implementing appropriate technical and organisational measures to integrate data-protection principles like data minimisation into processing. AI Act Article 10 requires training data to meet quality criteria with appropriate governance: design choices, data collection processes, annotation, labelling, cleaning, bias detection and mitigation. When the AI processes personal data, GDPR by-design requirements and AI Act data governance must be implemented jointly: the AI system's data pipeline must simultaneously ensure GDPR data minimisation AND AI Act data quality, which can create tension since minimisation limits data while quality often requires more diverse data.",
        "predicted_answer": "Based on the provided context, GDPR Article 25 (data protection by design) and AI Act Article 10 (data governance) are directly aligned and mutually reinforcing.\n\nThe context explicitly states that **AI Act Art. 10 data governance must reconcile with GDPR Art. 25 by-design principles**, with the following specific mappings:\n\n- **Data governance (Art. 10)** should implement GDPR Art. 25 measures\n- **Accuracy/robustness (Art. 15)** overlaps with GDPR's accuracy and integrity principles\n- **Transparency (Art. 13)** supports GDPR's transparency by design\n- **Risk management (Art. 9)** should integrate data protection risks\n\nAdditionally, there is a **fundamental tension** between the two frameworks: AI Act Art. 10 requires training data to be sufficiently representative (requiring more data), while GDPR Art. 25's data minimisation principle requires only data that is strictly necessary. Resolution approaches include synthetic data, federated learning, and differential privacy.\n\nThe recommended practical approach is to use a **unified \"responsible AI\" framework** that satisfies",
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        "total_tokens": 881426,
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      },
      {
        "question_id": "MG-B07",
        "question": "What are the supply chain implications when a financial institution uses a third-party high-risk AI system, considering both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 25, if the financial institution modifies the AI system substantially or changes its intended purpose, it becomes the provider and assumes ALL provider obligations. Under DORA Articles 28-44, contractual arrangements with third-party ICT providers must include: clear description of services, availability and integrity provisions, exit strategies and transition plans. Critical third-party providers are subject to Union oversight. When combined: the financial entity must maintain DORA third-party oversight AND ensure the AI system meets AI Act high-risk requirements, with the risk that any modification to fit the financial use case could trigger full provider status under the AI Act.",
        "predicted_answer": "## Supply Chain Implications for Financial Institutions Using Third-Party High-Risk AI\n\n### AI Act Obligations\n\nUnder **Article 26**, the financial institution (as deployer) must:\n- Use the system per the provider's instructions\n- Assign competent human oversight\n- Monitor operation and report serious incidents within **15 days**\n- Conduct a **GDPR Art. 35 DPIA** using the provider's Art. 13 documentation\n\nIf the institution fine-tunes the model or makes a **substantial modification**, it becomes a **provider** under Art. 25, triggering full Art. 9\u201315 compliance obligations.\n\n### DORA Obligations\n\nUnder **Articles 28\u201330**, the third-party AI provider is an **ICT third-party service provider**, requiring:\n- Contractual safeguards including SLAs, data residency, audit rights, and exit strategies\n- Ongoing monitoring of AI service performance\n- Incident notification obligations from the AI provider to the institution\n- If designated a **critical TPSP** by ESAs, direct oversight applies\n\n### Combined Supply Chain Structure\n\nThe full chain creates layered responsibilities:",
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      },
      {
        "question_id": "MG-B08",
        "question": "How do AI Act risk management requirements interact with DORA ICT risk management for credit scoring AI?",
        "gold_answer": "Credit scoring AI is classified as high-risk under Annex III point 5(b). Under AI Act Article 9, the provider must implement a continuous iterative risk management system throughout the system's lifecycle, covering known and foreseeable risks with residual risk kept to acceptable levels. Under DORA Articles 5-16, the financial entity must maintain an ICT risk management framework with strategies, policies, procedures and protocols. These two requirements must be integrated: the AI-specific risk management (bias, accuracy, fairness) must be embedded within the broader DORA ICT risk framework, with the AI risk management system feeding into DORA's systematic documentation and the DORA framework providing the organisational infrastructure for AI risk governance.",
        "predicted_answer": "Based on the provided context, for credit scoring AI (Annex III Category 5(b)), both frameworks apply simultaneously and overlap significantly.\n\n**Dual Obligation:** Financial entities must maintain **both** an AI Act Article 9 risk management system (covering AI-specific risks to health, safety, and fundamental rights) **and** a DORA Articles 5-7 ICT risk management framework (covering ICT infrastructure risks). The AI system must be classified as an ICT asset under DORA.\n\n**Key Interactions:**\n- The AI Act risk management feeds into GDPR Article 35 DPIA requirements, while DORA requires the ICT risk framework to be integrated into the entity's overall risk management system\n- DORA requires business continuity and manual fallback procedures for AI system failures\n- Model updates and retraining must follow DORA ICT change management procedures\n- AI Act Article 15 cybersecurity requirements must also satisfy DORA's digital operational resilience testing (Articles 24-27), including threat-led penetration testing\n\n**Practical Approach:** The context explicitly recommends creating a **unified risk register** that maps each risk to its",
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      },
      {
        "question_id": "MG-B09",
        "question": "What dual incident notification requirements apply when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2?",
        "gold_answer": "Under AI Act Article 73, the provider must report the serious incident to market surveillance authorities within 15 days of establishing a causal link. Under NIS2 Article 23, the essential entity must notify the CSIRT: within 24 hours (early warning), within 72 hours (incident notification with severity assessment), and within one month (final report with root cause). The NIS2 timelines are much more aggressive than the AI Act's 15-day deadline. Both notifications go to different authorities (market surveillance vs. CSIRT) with different templates and different information requirements. The entity must coordinate both reporting streams simultaneously.",
        "predicted_answer": "Based on the provided context, the dual incident notification requirements are:\n\n**AI Act (Article 73):** Providers must report serious incidents to market surveillance authorities **within 15 days** of becoming aware. A cybersecurity breach affecting critical infrastructure would qualify as a serious incident under Art. 73(b) (\"serious and irreversible disruption of critical infrastructure\").\n\n**NIS2 (Article 23):** Essential entities must report significant incidents in **three stages**:\n- Early warning within **24 hours**\n- Full incident notification within **72 hours**\n- Final report within **one month**\n\nThese go to the **CSIRT or national competent authority** \u2014 a different authority than the AI Act's market surveillance authority.\n\nThe context also notes that if the entity is a financial institution, **DORA's 4-hour initial notification** requirement would apply simultaneously, making it the fastest deadline of all. If personal data is involved, **GDPR's 72-hour** breach notification to the DPA adds a fourth obligation.\n\nCritically, the NIS2 \"significant incident\" threshold is **lower** than the AI Act's \"serious incident\" threshold, meaning",
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      },
      {
        "question_id": "MG-B10",
        "question": "How does the AI Act's conformity assessment interact with existing product safety legislation for AI-embedded products?",
        "gold_answer": "Under Article 6(1), AI systems that are safety components of products covered by Annex I Section A harmonisation legislation are subject to the same conformity assessment as the parent legislation. Under Article 43, for stand-alone high-risk AI in Annex III, providers may use internal control (Annex VI) or notified body assessment (Annex VII). For Annex I products, AI Act requirements are integrated INTO the existing product conformity assessment \u2014 not a separate process. The CE marking (Art. 48) covers both the product safety and AI compliance. Any substantial modification triggers a new conformity assessment covering both dimensions.",
        "predicted_answer": "Based on the provided context, for AI systems that are safety components of products covered by **Annex I Union harmonisation legislation** (e.g., medical devices, machinery, toys), the conformity assessment must be integrated with existing product conformity requirements.\n\nSpecifically:\n\n- A system is **high-risk** when it is a safety component of an Annex I product **and** that product requires **third-party conformity assessment** (Article 6)\n- For such systems, **notified body assessment is mandatory** (Annex VII) \u2014 self-assessment is not permitted\n- The CE marking process requires completing the AI Act conformity assessment **alongside** the existing product conformity assessment (e.g., MDR for medical devices)\n- The notified body for AI assessment **may differ** from the product's notified body, requiring coordination between the two processes\n\nAs the context notes with a concrete example: *\"An AI diagnostic system must satisfy BOTH AI Act and MDR conformity requirements\"* and *\"coordination is essential.\"*\n\nThe practical implication is that organizations must run **parallel conformity tracks** \u2014 one under the existing product legislation and one under the AI Act \u2014 ensuring",
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      {
        "question_id": "MG-C01",
        "question": "A bank deploys a credit scoring AI system that experiences a data breach affecting 50,000 customers. Map out ALL regulatory obligations, timelines, authorities, and potential penalties across every applicable framework.",
        "gold_answer": "This triggers obligations under four frameworks simultaneously: (1) AI Act: Report serious AI incident to market surveillance within 15 days (Art. 73). Potential penalty: EUR 15M or 3% turnover for Chapter III violations (Art. 99). (2) GDPR: Notify supervisory authority within 72 hours and affected data subjects without undue delay (Art. 33-34). Penalty: up to EUR 20M or 4% turnover (Art. 83). (3) DORA: Report major ICT incident to financial supervisory authority with classification by impact (Art. 17-23). Penalty: per national implementation. (4) NIS2: If the bank is an essential entity, notify CSIRT within 24 hours (early warning) and 72 hours (full notification) (Art. 23). Penalty: up to EUR 10M or 2% turnover (Art. 34). Maximum cumulative penalty exposure: up to 9% of global turnover (3% AI Act + 4% GDPR + 2% NIS2). Four different authorities must be notified with four different timelines and templates.",
        "predicted_answer": "## Multi-Framework Regulatory Obligations for AI Credit Scoring Data Breach\n\n### Applicable Frameworks\n\nA bank deploying a credit scoring AI system falls under **all four regulatory frameworks simultaneously**: AI Act (Annex III Cat. 5b), DORA, NIS2 (banking = essential entity), and GDPR.\n\n---\n\n## Reporting Obligations & Timelines\n\n### T+4 Hours \u2014 DORA (Fastest Deadline)\n- **Obligation**: Initial notification to financial competent authority (e.g., ECB, BaFin)\n- **Authority**: Financial supervisory authority\n- **Content**: Incident classification, initial assessment\n- **Basis**: DORA Art. 19\n\n### T+24 Hours \u2014 NIS2\n- **Obligation**: Early warning to CSIRT/competent authority\n- **Authority**: National CSIRT\n- **Content**: Whether incident involves malicious/unlawful acts\n- **Basis**: NIS2 Art. 23(a)\n\n### T+72 Hours \u2014 GDPR + DORA Intermediate\n- **GDPR**: Notify D",
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        "f1": 0.6,
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        "total_tokens": 1162619,
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      },
      {
        "question_id": "MG-C02",
        "question": "An energy utility deploys AI for grid management (critical infrastructure). A third-party provider substantially modifies the model. Who bears which obligations under the AI Act, DORA, and NIS2?",
        "gold_answer": "Under AI Act Article 25, the third party that substantially modifies the system becomes the provider and assumes ALL provider obligations: risk management (Art. 9), data governance (Art. 10), documentation (Art. 11), conformity assessment (Art. 43), CE marking (Art. 48). The energy utility remains the deployer with Art. 26 obligations: human oversight, monitoring, incident reporting. Under NIS2, the energy utility as essential entity must maintain cybersecurity measures (Art. 21) and incident notification (Art. 23) regardless of who is the AI provider. Under DORA (if applicable as financial-adjacent), third-party management (Art. 28-44) requires contractual arrangements covering the modification scope. Critical infrastructure AI is high-risk under Annex III point 2, so ALL Chapter III requirements apply. The modification creates a liability gap: the original provider is no longer responsible, but the modifier may not have the original's technical infrastructure.",
        "predicted_answer": "## Obligation Allocation: Energy Utility AI Grid Management\n\n### AI Act Obligations\n\n**Third-party provider (after substantial modification):**\nUnder Article 25, when a deployer makes a **substantial modification**, they become a **provider**. The third-party provider therefore assumes full provider obligations:\n- Risk management system (Art. 9)\n- Data governance (Art. 10)\n- Technical documentation (Art. 11)\n- Human oversight design (Art. 14)\n- Accuracy, robustness, and cybersecurity (Art. 15)\n- Quality management system (Art. 17)\n- Conformity assessment (Art. 43) \u2014 as Annex III Category 2 (critical infrastructure), this may require third-party assessment\n- CE marking (Art. 48) and EU database registration (Art. 49)\n\n**Energy utility (deployer):**\n- Use system per instructions for use (Art. 26(a))\n- Assign competent human oversight persons (Art. 26(b))\n- Monitor operation and report serious incidents within **15 days** (Art. 26(e), Art.",
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        "total_tokens": 1226953,
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      {
        "question_id": "MG-C03",
        "question": "Design a comprehensive compliance program for a hospital deploying an AI diagnostic system, addressing the AI Act, GDPR, NIS2, and fundamental rights. What are the key tensions between these frameworks?",
        "gold_answer": "The hospital must: (1) AI Act: The diagnostic AI is high-risk (Annex III \u2014 health). Ensure provider has completed conformity assessment (Art. 43), implement human oversight with qualified medical staff (Art. 14), monitor system operation (Art. 26), report incidents (Art. 73). (2) GDPR: Conduct DPIA (Art. 35) for health data processing, ensure lawful basis (likely explicit consent or vital interests), comply with data minimisation while ensuring sufficient training data quality. (3) NIS2: Hospital as essential entity must maintain cybersecurity framework (Art. 21), report AI-related security incidents to CSIRT (Art. 23). (4) Fundamental Rights: Conduct FRIA (Art. 27) assessing discrimination risks in diagnostic outcomes. Key tensions: GDPR data minimisation vs AI Act data quality requirements (Art. 10); human oversight (Art. 14) vs clinical workflow efficiency; transparency (Art. 13) vs medical liability if clinicians over-rely on explained AI outputs; FRIA/DPIA duplication burden. Resolution: combine FRIA and DPIA, establish clinical AI governance committee, implement tiered human oversight based on diagnostic confidence levels.",
        "predicted_answer": "## Comprehensive Compliance Program: Hospital AI Diagnostic System\n\n### Regulatory Classification\n\n**AI Act**: The diagnostic AI system qualifies as high-risk under **Annex III Category 5** (essential services/health) and likely also as a **safety component of a medical device** under Annex I, triggering mandatory **third-party conformity assessment** (Art. 43).\n\n**GDPR**: Health data is a **special category** under Art. 9, requiring explicit consent or substantial public interest legal basis.\n\n**NIS2**: Hospitals are **essential entities** under the health sector, subject to full Art. 21 cybersecurity requirements and Art. 23 incident reporting.\n\n**FRIA**: As a body governed by public law providing public services, the hospital must conduct a **Fundamental Rights Impact Assessment** before deployment (Art. 26(1a)).\n\n---\n\n### Phase 1: Pre-Deployment Requirements\n\n#### AI Act Obligations\n\n- Establish **risk management system** (Art. 9) covering diagnostic failure scenarios, misdiagnosis risks, and foreseeable misuse (e.g., using the system outside its validated patient",
        "exact_match": 0.0,
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        "latency_ms": 6273.07859999928,
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        "total_tokens": 1288818,
        "convergence_rounds": 1,
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      },
      {
        "question_id": "MG-C04",
        "question": "What is the total maximum fine exposure for a tech company that develops a GPAI model with systemic risk, violates prohibited AI practices, fails GDPR data protection, and breaches NIS2 cybersecurity?",
        "gold_answer": "Maximum cumulative fine exposure across all frameworks: AI Act prohibited practices (Art. 5/99): EUR 35M or 7% of worldwide turnover. AI Act GPAI systemic risk violations (Art. 51-55/99): EUR 15M or 3% of turnover. GDPR basic principles violation (Art. 83): EUR 20M or 4% of turnover. NIS2 essential entity (Art. 34): EUR 10M or 2% of turnover. Total maximum: 16% of worldwide annual turnover (7% + 3% + 4% + 2%), or EUR 80 million (35M + 15M + 20M + 10M), whichever is higher for each individual fine. Each fine is assessed independently by different authorities under different frameworks. The ne bis in idem principle may limit overlap within the same jurisdiction, but cross-framework fines from different regulatory regimes are generally cumulative.",
        "predicted_answer": "Based on the provided context, the maximum cumulative fine exposure would be:\n\n- **AI Act Art. 99(3)** \u2013 Prohibited AI practices: **7% of worldwide annual turnover**\n- **GDPR Art. 83(5)** \u2013 Data protection violations: **4% of worldwide annual turnover**\n- **NIS2 Art. 34** \u2013 Cybersecurity breaches (if essential entity): **2% of worldwide annual turnover**\n\n**Total theoretical maximum: approximately 13% of global annual turnover** (plus any DORA penalties if applicable to financial services).\n\nThe context also notes that AI Act Art. 99(7) requires coordination between AI Act authorities and DPAs, meaning fines are **coordinated but not necessarily deduplicated** \u2014 both can apply for the same conduct. Additionally, NIS2 Art. 20(2) allows for **personal liability of management body members**, adding further exposure beyond corporate fines.",
        "exact_match": 0.0,
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        "total_tokens": 1352030,
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      },
      {
        "question_id": "MG-C05",
        "question": "How should a multinational deploy an AI recruitment system across the EU, considering the AI Act's territorial scope, GDPR's cross-border processing rules, and varying NIS2 transposition?",
        "gold_answer": "AI Act Article 2 applies to all deployers located in the Union, regardless of provider location. Recruitment AI is high-risk under Annex III point 4. The deployer must: ensure conformity assessment from the provider (Art. 43), implement human oversight per Art. 14 (qualified HR staff in each jurisdiction), and conduct FRIA (Art. 27) for each country's specific population. GDPR requires: identifying a lead supervisory authority for cross-border processing, conducting DPIAs per Art. 35, ensuring lawful basis (likely legitimate interest or consent), and respecting Art. 22 automated decision-making rights in each jurisdiction. NIS2 transposition varies by Member State \u2014 the company must verify whether it is classified as essential/important entity in each country where it operates. Key challenge: a recruitment decision valid under one Member State's GDPR interpretation may violate another's, and the AI Act's uniform high-risk requirements may conflict with varying national employment laws.",
        "predicted_answer": "## Deploying an AI Recruitment System Across the EU: Multi-Regulatory Analysis\n\n### 1. AI Act Territorial Scope\n\nThe AI Act applies to **any provider placing the system on the EU market and any deployer located within the EU**, regardless of where the multinational is headquartered. Under Article 2, if the system's output is used in the Union, the Act applies even to non-EU entities. A non-EU parent company must designate an **EU authorized representative** before deployment.\n\nThe recruitment AI falls under **Annex III Category 4** (employment, workers management, recruitment), making it a **high-risk AI system** requiring full compliance with Articles 9\u201315, conformity assessment (Article 43), CE marking (Article 48), and EU database registration (Article 49).\n\n---\n\n### 2. AI Act Compliance Requirements\n\nBefore any EU deployment, the multinational must:\n\n- Establish a **risk management system** (Article 9) covering foreseeable misuse scenarios (e.g., using a CV-screening tool to monitor employee movements)\n- Implement **data governance** (Article 10) ensuring training data is representative",
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        "total_tokens": 1418787,
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      {
        "question_id": "MG-C06",
        "question": "A law enforcement agency wants to use real-time biometric identification in a public space to prevent an imminent terrorist threat. What is the complete legal pathway, conditions, and oversight requirements across all applicable frameworks?",
        "gold_answer": "AI Act Article 5 generally prohibits real-time remote biometric identification in public spaces for law enforcement. Exception: strictly necessary for prevention of specific, substantial and imminent threat to life or genuine terrorist threat. Conditions: must be authorised by judicial authority or independent administrative authority; limited in time, geographic and personal scope; subject to prior fundamental rights impact assessment; registered in the EU database. Article 14 requires verification by at least two natural persons. Biometric identification is high-risk Annex III point 1 \u2014 full Chapter III requirements apply. GDPR applies: processing of biometric data (special category, Art. 9) requires explicit legal basis, likely Art. 9(2)(g) substantial public interest. GDPR Art. 35 DPIA required before deployment. If the system is embedded in essential infrastructure, NIS2 Art. 21 cybersecurity measures apply. The complete chain: judicial authorisation \u2192 FRIA \u2192 DPIA \u2192 conformity verified \u2192 human oversight (2 persons) \u2192 time-limited deployment \u2192 incident reporting to multiple authorities.",
        "predicted_answer": "## Legal Pathway for Real-Time Biometric Identification for Imminent Terrorist Threat Prevention\n\n### 1. AI Act Article 5 \u2014 Prohibited Practice Exception\n\nReal-time remote biometric identification in publicly accessible spaces for law enforcement is **prohibited by default** under Article 5(d), but an explicit exception exists for **\"prevention of imminent threat to life, or genuine terrorist threat.\"**\n\n**Conditions to invoke the exception:**\n- The threat must be **genuine and imminent** \u2014 not speculative or precautionary\n- Use must be **strictly necessary** for the specific threat\n- No transition period or general authorization; each deployment must be justified\n\n---\n\n### 2. Conformity Assessment Requirements\n\nBecause this is a **biometric identification system (Annex III, Category 1)**, it requires:\n- **Mandatory third-party conformity assessment** by a notified body (Article 43) \u2014 self-assessment is not permitted for biometric identification systems\n- Full compliance with **Articles 9\u201315**: risk management, data governance, transparency, human oversight, accuracy/robustness, and cybersecurity\n- **CE marking** (Article",
        "exact_match": 0.0,
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        "latency_ms": 6123.039499998413,
        "cost_usd": 0.06641999999999992,
        "total_tokens": 1488661,
        "convergence_rounds": 1,
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      {
        "question_id": "MG-C07",
        "question": "An insurance company uses AI for risk assessment and a data protection authority finds the training data was biased against certain ethnic groups. Trace the full enforcement cascade across AI Act, GDPR, and potential liability.",
        "gold_answer": "Insurance risk assessment AI is high-risk under Annex III point 5 (essential services). Enforcement cascade: (1) AI Act Art. 10 violation \u2014 data governance failed to detect and mitigate bias in training data. Art. 9 risk management failed to identify discrimination risk. Penalties: EUR 15M or 3% turnover. (2) GDPR Art. 5 violation \u2014 fairness principle breached through discriminatory processing. Art. 22 \u2014 automated decisions with significant effects lacked adequate safeguards. Penalties: EUR 20M or 4% turnover. (3) Fundamental Rights \u2014 Art. 27 FRIA should have identified discrimination risk against ethnic groups, linked to EU Charter Art. 21 non-discrimination. (4) Product Liability \u2014 affected individuals may bring civil claims for discriminatory insurance pricing. (5) Corrective cascade: market surveillance authority can order system withdrawal (AI Act), data protection authority can order processing halt (GDPR), and the provider must conduct new conformity assessment after fixing the bias. Combined exposure: 7% turnover (3%+4%) plus civil liability.",
        "predicted_answer": "## Full Enforcement Cascade: Biased AI Risk Assessment in Insurance\n\n### 1. AI Act Violations\n\n**Classification**: Insurance risk assessment AI falls under **Annex III Category 5(c)** \u2014 explicitly listed as high-risk.\n\n**Primary violations**:\n- **Article 10** (Data Governance): Training data failed to be \"free of errors and complete\" and failed examination of \"possible biases likely to affect health and safety or fundamental rights\"\n- **Article 9** (Risk Management): Failure to identify and analyse known/foreseeable risks to fundamental rights\n- **Article 15** (Accuracy/Robustness): System failed to achieve appropriate accuracy across demographic groups\n\n**Penalty**: Up to **EUR 15 million or 3% of worldwide annual turnover** (Art. 99(4))\n\n**Additional obligation**: As a deployer of insurance risk assessment AI, the company was required to conduct a **Fundamental Rights Impact Assessment (FRIA)** under Art. 26(1a) before deployment. Failure to do so constitutes a separate violation.\n\n---\n\n### 2. GDPR Violations\n\n**Article",
        "exact_match": 0.0,
        "f1": 0.36363636363636365,
        "latency_ms": 5749.920300000667,
        "cost_usd": 0.06642300000000034,
        "total_tokens": 1564696,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-C08",
        "question": "How does the AI Act's quality management system requirement interact with DORA's ICT governance and NIS2's cybersecurity policies for a fintech company running AI-based fraud detection?",
        "gold_answer": "The fintech faces three overlapping governance frameworks: AI Act Art. 17 QMS: requires documented strategies for regulatory compliance, design procedures, quality control, testing, data management, risk management, post-market monitoring, incident reporting, and resource management. DORA ICT Governance (Art. 5-16): requires internal governance framework for ICT risk with strategies, policies, procedures, and protocols covering all ICT assets. NIS2 Art. 21: requires policies on risk analysis, incident handling, business continuity, supply chain security, and cybersecurity training. Integration approach: the QMS should be the umbrella system incorporating both DORA ICT risk management and NIS2 cybersecurity measures as sub-frameworks. The AI-specific risk management (bias, accuracy, fairness for fraud detection) feeds into the broader ICT risk assessment, while NIS2 cybersecurity requirements map to AI Act Art. 15 (accuracy, robustness, cybersecurity). Duplication risk: without integration, the fintech maintains three separate but overlapping governance systems.",
        "predicted_answer": "## Interaction of AI Act QMS, DORA ICT Governance, and NIS2 Cybersecurity for a Fintech's AI Fraud Detection System\n\n### Three Overlapping Frameworks\n\nA fintech running AI-based fraud detection must simultaneously satisfy:\n\n1. **AI Act Article 17 (QMS)** \u2014 requires documented policies, procedures, and instructions covering the entire AI lifecycle, including risk management (Art. 9), post-market monitoring (Art. 72), and incident reporting\n2. **DORA Articles 5-7 (ICT Risk Management)** \u2014 requires an ICT risk management framework integrated into overall risk management, with the management body directly responsible\n3. **NIS2 Article 21 (Cybersecurity Risk Management)** \u2014 requires technical, operational, and organizational measures including supply chain security, vulnerability handling, and incident handling\n\n### Why All Three Apply\n\nThe fraud detection AI system is:\n- **High-risk under AI Act Annex III Category 6** (law enforcement-adjacent risk assessment) or potentially **Category 5** (essential services), triggering full Art. 9-17 obligations\n- **",
        "exact_match": 0.0,
        "f1": 0.50187265917603,
        "latency_ms": 5282.291300001816,
        "cost_usd": 0.06641399999999997,
        "total_tokens": 1667463,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-C09",
        "question": "Compare and contrast the incident reporting timelines, authorities, and templates across AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank.",
        "gold_answer": "For a single AI incident affecting a critical infrastructure bank, four parallel reporting obligations trigger: AI Act (Art. 73): Report to market surveillance authority within 15 days of establishing causal link. Content: AI system details, nature of incident, corrective measures. GDPR (Art. 33): Notify data protection authority within 72 hours of becoming aware. Content: nature of breach, categories of data subjects, likely consequences, mitigation measures. DORA (Art. 17-23): Report to financial supervisory authority per incident classification. Content: standardised template with clients affected, duration, geographic spread, data loss. NIS2 (Art. 23): Notify CSIRT within 24 hours (early warning), 72 hours (full notification), one month (final report). Content: suspected malicious cause, severity, impact, root cause. Comparison: NIS2 has fastest deadline (24h), GDPR next (72h), AI Act slowest (15 days). Four different authorities, four different templates, four different legal bases. The bank's incident response team needs a unified procedure mapping one event to four streams.",
        "predicted_answer": "## Incident Reporting Comparison for a Critical Infrastructure Bank AI Incident\n\n### Timeline Overview\n\n| Regulation | Initial Report | Intermediate | Final | Authority |\n|------------|---------------|--------------|-------|-----------|\n| **DORA** | 4 hours (initial notification) | 72 hours | 1 month | Financial competent authority (ECB, BaFin, etc.) |\n| **NIS2** | 24 hours (early warning) | 72 hours | 1 month | CSIRT/competent authority |\n| **GDPR** | 72 hours | N/A | N/A (+ individual notification if high risk) | National DPA |\n| **AI Act** | \"Immediately, not later than 15 days\" | N/A | N/A | Market surveillance authority |\n\n### Fastest to Slowest Deadlines\n1. **DORA** \u2014 4 hours (most urgent)\n2. **NIS2** \u2014 24 hours early warning\n3. **GDPR** \u2014 72 hours\n4. **AI Act** \u2014 15",
        "exact_match": 0.0,
        "f1": 0.45454545454545453,
        "latency_ms": 7014.100000000326,
        "cost_usd": 0.06641700000000039,
        "total_tokens": 1726126,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-C10",
        "question": "An AI regulatory sandbox participant wants to test a novel emotion recognition system in a workplace setting. What are the legal boundaries, exceptions, and framework interactions?",
        "gold_answer": "This faces a direct prohibition: AI Act Article 5 prohibits emotion recognition in workplace and educational institutions. Regulatory sandboxes (Art. 57-58) provide controlled testing environments, but Article 5 prohibitions are absolute \u2014 no sandbox exception. The only permitted emotion recognition is outside workplace/education contexts. If the participant proposes a modified scope (e.g., public safety context), then: AI Act: must operate within sandbox plan agreed with competent authority; GDPR: emotion recognition processes biometric data (special category, Art. 9) requiring explicit consent or substantial public interest; GDPR Art. 35 DPIA mandatory; fundamental rights assessment required for any biometric categorisation. Even in a sandbox, GDPR protections apply fully \u2014 sandbox does not exempt from data protection law. NIS2 applies if the sandbox operator is an essential entity. Bottom line: workplace emotion recognition cannot be tested even in a sandbox. The prohibition is not a regulatory barrier that sandboxes can bypass \u2014 it's a fundamental rights protection.",
        "predicted_answer": "## Legal Analysis: Emotion Recognition AI in Workplace Sandbox Testing\n\n### Core Prohibition \u2014 Article 5\n\nThe proposed testing faces a **fundamental legal barrier**: emotion recognition in workplace settings is an **outright prohibited practice** under Article 5(f) of the AI Act. This prohibition:\n\n- Applies from **2 February 2025**\n- Carries penalties up to **EUR 35 million or 7% of worldwide annual turnover**\n- **Cannot be circumvented by sandbox participation**\n\n### Sandbox Does Not Override Article 5\n\nCritically, Article 57(1) explicitly states that AI regulatory sandboxes **do not affect supervisory and corrective powers**, and Article 5 prohibited practices **remain prohibited even within a sandbox**. The sandbox cannot provide a legal pathway to test what the law categorically bans.\n\n### Additional Regulatory Barriers\n\nEven if the Article 5 prohibition did not exist, the system would face:\n\n1. **GDPR Article 9**: Emotion recognition likely processes biometric data (special category), requiring explicit consent or another Article 9(2) exception\n2. **GDPR Article 35**: A D",
        "exact_match": 0.0,
        "f1": 0.625,
        "latency_ms": 9823.481300001731,
        "cost_usd": 0.06638400000000022,
        "total_tokens": 1779409,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      }
    ]
  },
  "cognigraph-pcst-v2": {
    "summary": {
      "method": "cognigraph-pcst-v2",
      "dataset": "MultiGov-30-v3-claude-sonnet-4-6",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.4372285568405694,
      "avg_latency_ms": 201557.74787333354,
      "avg_cost_usd": 0.19106320000000016,
      "avg_tokens": 181.96666666666667,
      "avg_rounds": 2.033333333333333,
      "avg_nodes": 10.0,
      "total_cost_usd": 5.731896000000004,
      "total_latency_s": 6046.732436200006,
      "total_shacl_pass": 5330,
      "total_shacl_fail": 4010,
      "total_constraint_propagations": 0,
      "total_observer_redirects": 0,
      "total_ontology_route_filtered": 0
    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "Under Article 5 of the EU AI Act, the following AI practices are completely prohibited: subliminal or manipulative techniques causing significant harm (Art. 5(a)); exploitation of vulnerabilities based on age, disability, or social situation (Art. 5(b)); social scoring by public authorities (Art. 5(c)); real-time biometric identification in public spaces (Art. 5(d)); untargeted facial image scraping (Art. 5(e)); emotion recognition in workplaces and educational settings (Art. 5(f)); and biometric categorisation using sensitive characteristics (Art. 5(g)). These prohibitions became enforceable on 2 February 2025, with no transition period, meaning systems falling within these categories required immediate decommissioning rather than a phased wind-down. Violations carry penalties of up to \u20ac35 million or 7% of global annual turnover, whichever is higher, representing the most severe penalty tier under the Act. Organisations should note that compliance cannot be assumed without documented evidence of decommissioning or confirmed non-applicability, as enforcement is active from the February 2025 date.",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 248683.9837999978,
        "cost_usd": 0.26639700000000016,
        "total_tokens": 187,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 20,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "Based on the available expert analysis, a complete compliance chain for placing a high-risk AI system on the EU market cannot be fully synthesized here, as the domain expert consulted covers only Article 5 prohibited practices and explicitly lacks validated authority over Articles 9\u201317, which govern the core conformity assessment, technical documentation, human oversight, and registration requirements that constitute that chain. What can be stated with confidence is that any high-risk system must first be screened against Article 5 prohibitions before any further compliance steps are relevant \u2014 if a system triggers those prohibitions, no conformity pathway exists and decommissioning is immediate, with penalties reaching \u20ac35 million or 7% of global annual turnover. Proceeding to describe the Articles 9\u201317 compliance chain without validated expert input on those provisions would risk presenting unverified compliance guidance, which this synthesis framework does not permit. To answer this query completely and responsibly, additional validated expert analyses covering the high-risk system requirements under Articles 9 through 17 \u2014 including conformity assessments, notified body involvement where required, CE marking, and EU database registration \u2014 would need to be provided.",
        "exact_match": 0.0,
        "f1": 0.6,
        "latency_ms": 194120.38940000275,
        "cost_usd": 0.18549300000000019,
        "total_tokens": 201,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 10,
        "shacl_fail": 30,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "The expert analysis consulted falls outside the relevant domain and explicitly does not cover deployer obligations for high-risk AI systems. Based on the governance referral provided, the applicable provisions are **Article 26** (deployer obligations) and **Article 14** (human oversight measures) of the EU AI Act, but no validated expert output on the substance of those articles was returned in this synthesis. Providing specific obligations, penalties, or thresholds from Articles 14 and 26 without a validated domain expert analysis would risk stating unverified compliance claims, which these governance constraints prohibit. To obtain a reliable answer, this query should be directed to an agent with confirmed authority over Article 14 and Article 26 of the EU AI Act.",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 188546.3864000012,
        "cost_usd": 0.19104300000000018,
        "total_tokens": 163,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 17,
        "shacl_fail": 43,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "The sole expert analysis provided explicitly states it cannot address this query, as its domain is restricted to Article 5 prohibited practices, and it directs the question to agents scoped to Title VIII (Articles 51\u201355). Because no validated expert output covering GPAI systemic risk requirements was actually supplied, synthesizing a direct answer with cited article numbers, penalties, or thresholds would require me to generate information beyond what the governance-constrained analyses support. To obtain a compliant synthesis, you would need to resubmit this query with outputs from an expert agent scoped to Articles 51\u201355 of the EU AI Act, which govern model evaluation, adversarial testing, incident reporting, and cybersecurity obligations for providers of general-purpose AI models posing systemic risk.",
        "exact_match": 0.0,
        "f1": 0.6666666666666666,
        "latency_ms": 184493.60030000025,
        "cost_usd": 0.169674,
        "total_tokens": 159,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 28,
        "shacl_fail": 52,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "The available expert analysis does not cover GDPR data processing principles directly, as the contributing domain expert was scoped exclusively to EU AI Act Article 5 prohibited practices and appropriately declined to address GDPR substance. Synthesizing a specific answer on GDPR's Article 5 principles (lawfulness, fairness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, and accountability) or their application to AI training would require drawing on unvalidated knowledge outside the confirmed expert outputs, which this synthesis must not do. What the validated analysis does confirm is that where AI training involves practices prohibited under EU AI Act Article 5 \u2014 such as untargeted scraping of facial images for biometric databases \u2014 GDPR violations are likely to co-occur, creating simultaneous liability under both frameworks. To obtain a properly governed answer on GDPR principles and AI training, a dedicated GDPR-domain expert analysis is required before synthesis can proceed responsibly.",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 130414.17700000238,
        "cost_usd": 0.1178729999999999,
        "total_tokens": 164,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 48,
        "shacl_fail": 52,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "Based on the available expert analysis, the primary transparency obligations for AI systems generating deepfakes or synthetic content fall under **EU AI Act Article 50**, which mandates that deployers of such systems ensure recipients are informed that content is artificially generated or manipulated \u2014 though the specific requirements, thresholds, and penalties under Art. 50 were outside the validated scope of the analysis provided. Where a deepfake system additionally employs subliminal or manipulative techniques causing significant harm to persons, it may simultaneously trigger the **Article 5(a) prohibition** on manipulative AI practices, which carries penalties of up to \u20ac35 million or 7% of global annual turnover. These two provisions can apply concurrently: Art. 50 governs the disclosure obligation regardless of intent, while Art. 5(a) addresses the harder prohibition where manipulation crosses into psychological harm. Because the Art. 50 analysis was not validated in the inputs provided, a definitive compliance determination on transparency obligations specifically cannot be confirmed here, and consultation of an Art. 50-specialized analysis is warranted before drawing compliance conclusions.",
        "exact_match": 0.0,
        "f1": 0.6,
        "latency_ms": 164142.39699999962,
        "cost_usd": 0.16097099999999975,
        "total_tokens": 175,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 61,
        "shacl_fail": 59,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
        "predicted_answer": "The available expert analysis cannot support a substantive answer to this query. The only validated input explicitly identifies DORA ICT risk management as outside its governance domain and declines to address it, appropriately directing to DORA Articles 5\u201316 as the relevant source. Synthesizing a direct answer with specific article numbers, penalties, or thresholds would require fabricating regulatory content not present in the validated expert outputs, which would violate the requirement to respect governance constraints and not claim compliance without evidence. To obtain accurate information on how DORA defines ICT risk management requirements for financial entities, you should consult a DORA-specific regulatory source or directly reference the Digital Operational Resilience Act (Regulation (EU) 2022/2554), particularly Articles 5 through 16.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 116277.01030000026,
        "cost_usd": 0.11493899999999946,
        "total_tokens": 163,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 81,
        "shacl_fail": 59,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
        "predicted_answer": "The available expert analysis cannot provide a validated answer to this query. The only contributing expert was scoped exclusively to EU AI Act Article 5 prohibited practices and explicitly declined jurisdiction over NIS2 incident notification timelines, which fall under Directive 2022/2555. No NIS2-scoped expert analysis was provided in the synthesis pool, meaning any specific citation of article numbers, timelines, or penalty thresholds from that directive would be fabricated rather than drawn from validated expert output. To obtain accurate NIS2 incident notification requirements, a governance agent scoped to Directive 2022/2555 should be consulted.",
        "exact_match": 0.0,
        "f1": 0.11842105263157895,
        "latency_ms": 126456.80260000154,
        "cost_usd": 0.11405399999999988,
        "total_tokens": 164,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 101,
        "shacl_fail": 59,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
        "predicted_answer": "The available expert analysis does not cover Annex III high-risk classifications, as the contributing domain expert was scoped exclusively to Article 5 prohibited practices. Accordingly, a direct answer on Annex III employment-related high-risk use cases cannot be provided without validated input from a high-risk classification agent, and claiming otherwise would violate the governance constraints under which these analyses operate. What can be confirmed is that certain employment-adjacent AI practices are addressed at the prohibition level under Article 5: emotion recognition in workplaces is absolutely prohibited under Art. 5(1)(f), and biometric categorisation based on sensitive characteristics is prohibited under Art. 5(1)(g), with violations carrying penalties of up to EUR 35 million or 7% of global annual turnover. For a complete and authoritative answer on Annex III employment use cases, this query should be directed to an expert with validated scope over high-risk AI system classifications.",
        "exact_match": 0.0,
        "f1": 0.15841584158415842,
        "latency_ms": 147283.7553999998,
        "cost_usd": 0.13653599999999977,
        "total_tokens": 162,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 118,
        "shacl_fail": 62,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
        "predicted_answer": "Based on the available expert analyses, a direct answer cannot be responsibly synthesized on this query. The sole contributing expert explicitly identified AI regulatory sandboxes under Articles 57-63 as outside its governed domain, and no validated analysis covering those provisions was returned. Fabricating substantive claims about sandbox scope, eligibility criteria, or operational timelines from Articles 57-63 would violate the governance constraint requiring evidence before asserting compliance or regulatory detail. If your question also touches Article 5 prohibited practices \u2014 for instance, whether sandbox participation affects obligations under Art. 5 that became enforceable on 2 February 2025 \u2014 that intersection could be addressed with confidence, but the sandbox framework itself requires input from an expert governing Articles 57-63 before a responsible synthesis is possible.",
        "exact_match": 0.0,
        "f1": 0.16666666666666666,
        "latency_ms": 131128.48329999906,
        "cost_usd": 0.11583900000000069,
        "total_tokens": 183,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 138,
        "shacl_fail": 62,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B01",
        "question": "How do the EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons?",
        "gold_answer": "GDPR Article 22 gives data subjects the right not to be subject to solely automated decisions with legal effects, with rights to human intervention, express their view, and contest decisions. The AI Act complements this: Article 26(7) requires deployers of high-risk AI systems making decisions about natural persons to inform them of the AI system's use. Article 13 requires sufficient transparency for deployers to interpret outputs. Article 14 mandates human oversight with override capability. Together, these create a layered protection: GDPR provides the right to contest, AI Act ensures transparency and human control over the decision process.",
        "predicted_answer": "The EU AI Act and GDPR operate simultaneously and create cumulative liability for automated decision-making affecting natural persons. AI Act Art. 5 prohibits manipulative or deceptive automated systems causing significant harm and bans biometric categorisation using sensitive characteristics, while GDPR Art. 22 grants individuals the right not to be subject to solely automated decisions producing significant legal or similarly significant effects, and GDPR Art. 9 provides heightened protection for biometric data as a special category. A violation of AI Act Art. 5 will likely trigger simultaneous GDPR breaches across both Art. 22 and Art. 9, meaning operators face compounding enforcement exposure under both regimes rather than a single penalty track. Financial exposure under the AI Act reaches up to \u20ac35 million or 7% of global annual turnover under Art. 99(3) for prohibited practice violations, layered on top of GDPR penalties of up to \u20ac20 million or 4% of global turnover under GDPR Art. 83(5). It should be noted that the precise interaction mechanisms between these frameworks remain subject to regulatory clarification, and compliance cannot be assumed solely from adherence to one instrument without independent assessment under the other.",
        "exact_match": 0.0,
        "f1": 0.2804428044280443,
        "latency_ms": 271217.59010000096,
        "cost_usd": 0.2557440000000004,
        "total_tokens": 183,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 138,
        "shacl_fail": 82,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B02",
        "question": "What are the combined penalty exposures when a high-risk AI system violates both the EU AI Act and GDPR?",
        "gold_answer": "Under GDPR Article 83, violations of basic principles or data subject rights face fines up to EUR 20 million or 4% of worldwide annual turnover. Under AI Act Article 99, Chapter III violations (high-risk requirements) face fines up to EUR 15 million or 3% of turnover, while Article 5 prohibited practices face EUR 35 million or 7%. These fines are cumulative \u2014 a single AI system that both violates GDPR data principles in its training data and fails AI Act high-risk requirements could face up to 7% (combined 4%+3%) of global turnover, or up to 11% if it also engages in prohibited practices (7%+4%).",
        "predicted_answer": "When a high-risk AI system violates Article 5 of the EU AI Act alongside GDPR provisions, the penalty exposures are cumulative rather than merged, creating a combined maximum liability of approximately 11% of global annual turnover. The EU AI Act's Article 5 prohibitions carry penalties up to \u20ac35 million or 7% of global annual turnover, while simultaneous GDPR violations under Article 9 (special category biometric data) and Article 22 (automated decision-making) add a separate exposure of up to \u20ac20 million or 4% of global annual turnover. These two frameworks apply simultaneously and independently, meaning regulators under each regime can pursue enforcement concurrently without one penalty offsetting the other. Enforcement of the EU AI Act's prohibited practices provisions became active on 2 February 2025 with no transition period, meaning organizations operating non-compliant systems after that date face immediate dual-framework exposure. It should be noted that the precise interaction between these regimes in live enforcement proceedings remains subject to regulatory and judicial interpretation, and confirmed compliance cannot be assumed without documented conformity assessments under both frameworks.",
        "exact_match": 0.0,
        "f1": 0.5714285714285714,
        "latency_ms": 321023.5208000013,
        "cost_usd": 0.265104,
        "total_tokens": 187,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 138,
        "shacl_fail": 102,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B03",
        "question": "How must a deployer using a high-risk AI system comply with both the AI Act's fundamental rights impact assessment and GDPR's data protection impact assessment?",
        "gold_answer": "The AI Act Article 27 requires public bodies and private entities providing public services to perform a Fundamental Rights Impact Assessment (FRIA) before deploying high-risk AI. GDPR Article 35 requires a DPIA when processing is likely to result in high risk to rights and freedoms. Both must identify risks, assess impact, and define mitigation measures. Deployers may combine the FRIA and DPIA to avoid duplication. The AI Act Article 26(9) specifically requires deployers to use the system's transparency documentation (Art. 13) to fulfill their GDPR DPIA obligations, creating a documented chain from provider's transparency documentation through to deployer's impact assessment.",
        "predicted_answer": "The query falls outside the validated domain of the contributing expert, which is limited to Article 5 prohibited practices under the AI Act, and no high-risk compliance agent output was provided; therefore, a fully evidenced synthesis cannot be responsibly constructed. What can be stated with confidence from the validated input is that where an AI system engages in practices touching biometric data or automated decision-making, the AI Act and GDPR operate simultaneously and cumulatively, with Art. 5 AI Act violations also triggering GDPR Art. 9 obligations for special category data processing and Art. 22 safeguards for solely automated decisions with significant effects. A deployer of a high-risk AI system would, based on established regulatory structure, need to coordinate any Fundamental Rights Impact Assessment required under the AI Act with a GDPR Data Protection Impact Assessment under GDPR Art. 35, which is mandatory where processing is likely to result in high risk to natural persons, but the precise procedural interaction, sequencing, and any consolidated assessment pathway cannot be confirmed without validated output from the High-Risk Requirements domain covering AI Act Arts. 9 through 17. Penalties under GDPR Art. 83 reach up to \u20ac20 million or 4% of global annual turnover, while AI Act non-compliance with high-risk obligations carries penalties up to \u20ac15 million or 3% of turnover, meaning dual exposure is a material risk. No compliance conclusion should be drawn from this response given the absence of validated expert coverage of the core high-risk obligations at issue.",
        "exact_match": 0.0,
        "f1": 0.22981366459627328,
        "latency_ms": 214453.96609999807,
        "cost_usd": 0.177315000000001,
        "total_tokens": 173,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 149,
        "shacl_fail": 111,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B04",
        "question": "What dual obligations apply when a financial entity reports an AI-related incident under both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 73, providers must report serious AI incidents to market surveillance authorities within 15 days of establishing a causal link. Under DORA Articles 17-23, financial entities must report major ICT incidents to the financial supervisory authority using standardised templates, with classification based on clients affected, duration, geographical spread, data losses, service criticality, and economic impact. Both reporting obligations apply simultaneously \u2014 to different authorities with different timelines and different templates. The financial entity must report the same AI incident twice: once to the market surveillance authority (AI Act, 15 days) and once to the financial supervisor (DORA, based on incident classification timeline).",
        "predicted_answer": "The available expert analysis cannot support a complete synthesis on this query. The only validated input addresses AI Act Article 5 prohibited practices, confirming that if an AI system involved in a financial incident constitutes a prohibited practice \u2014 such as manipulative AI or unlawful biometric categorisation \u2014 it triggers immediate decommissioning obligations with penalties up to \u20ac35 million or 7% of global turnover, but this represents only one dimension of the dual-obligation question. The DORA-specific incident reporting obligations, including their timelines, classification thresholds, and interaction mechanics with AI Act reporting requirements, fall outside the validated scope of the available analysis and cannot be responsibly characterized without a qualified DORA domain input. A complete answer to how these two frameworks apply simultaneously \u2014 including how an incident report filed under DORA's ICT-related incident regime intersects with AI Act supervisory notification duties \u2014 requires additional expert analysis that was not provided here.",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 188143.8780000026,
        "cost_usd": 0.1784099999999995,
        "total_tokens": 182,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 160,
        "shacl_fail": 120,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B05",
        "question": "How do the AI Act's cybersecurity requirements for high-risk AI interact with NIS2 security measures for essential entities?",
        "gold_answer": "AI Act Article 15 requires high-risk AI systems to be resilient against adversarial attacks including poisoning, model evasion, and confidentiality attacks, with technical measures to prevent, detect, respond to and control such attacks. NIS2 Article 21 requires essential entities to implement: risk analysis policies, incident handling, business continuity, supply chain security, vulnerability handling, cryptography and encryption, multi-factor authentication, and cybersecurity training. When a high-risk AI system is operated by an essential entity (e.g., critical infrastructure), both sets of requirements apply: the AI system must meet AI Act Art. 15 standards AND be integrated into the entity's NIS2 cybersecurity framework (Art. 21).",
        "predicted_answer": "The available expert analysis cannot support a complete synthesis on this query. The only validated output explicitly states it is scoped to EU AI Act Article 5 (prohibited practices) and lacks jurisdiction over the cybersecurity requirements in EU AI Act Article 15 or NIS2 Directive provisions \u2014 the two frameworks directly relevant to your question. Providing a synthesized answer on how Article 15's robustness and cybersecurity requirements for high-risk AI interact with NIS2's security obligations for essential entities (including NIS2's 24-hour incident notification timelines and potential fines up to \u20ac10 million or 2% of global turnover) would require me to generate content beyond what the validated expert analyses have confirmed, which would violate the governance constraint against claiming compliance without evidence. To obtain a reliable answer, you should consult validated outputs from agents scoped to EU AI Act Article 15 and the NIS2 Directive directly.",
        "exact_match": 0.0,
        "f1": 0.29310344827586204,
        "latency_ms": 137057.85209999885,
        "cost_usd": 0.11855700000000047,
        "total_tokens": 162,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 180,
        "shacl_fail": 120,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B06",
        "question": "How does the GDPR's data protection by design principle interact with the AI Act's data governance requirements?",
        "gold_answer": "GDPR Article 25 requires data protection by design \u2014 implementing appropriate technical and organisational measures to integrate data-protection principles like data minimisation into processing. AI Act Article 10 requires training data to meet quality criteria with appropriate governance: design choices, data collection processes, annotation, labelling, cleaning, bias detection and mitigation. When the AI processes personal data, GDPR by-design requirements and AI Act data governance must be implemented jointly: the AI system's data pipeline must simultaneously ensure GDPR data minimisation AND AI Act data quality, which can create tension since minimisation limits data while quality often requires more diverse data.",
        "predicted_answer": "The available expert analysis cannot support a complete synthesis on this query. The only validated input comes from an agent scoped to Article 5 prohibited practices of the EU AI Act, which explicitly flagged that GDPR Article 25 (data protection by design) and the AI Act's data governance requirements \u2014 most relevantly Article 10 (training data governance for high-risk AI systems) \u2014 fall outside its validated domain. What can be stated with confidence is that where a prohibited AI practice under Article 5 of the AI Act is involved, GDPR violations are also triggered simultaneously, meaning enforcement under both frameworks can proceed in parallel, with AI Act penalties reaching up to \u20ac35 million or 7% of global annual turnover and GDPR penalties reaching up to \u20ac20 million or 4% of global annual turnover under Article 83 GDPR. However, the substantive interaction between GDPR Article 25's privacy-by-design obligations and the AI Act's Article 10 data governance requirements for high-risk systems \u2014 including how they complement, duplicate, or create tension with one another \u2014 requires validated input from agents covering high-risk AI provisions and GDPR compliance architecture, which was not available here. Providing a confident synthesis beyond this boundary would risk misrepresenting the governance constraints under which these analyses operate.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 157348.11570000238,
        "cost_usd": 0.12772500000000075,
        "total_tokens": 179,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 199,
        "shacl_fail": 121,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-B07",
        "question": "What are the supply chain implications when a financial institution uses a third-party high-risk AI system, considering both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 25, if the financial institution modifies the AI system substantially or changes its intended purpose, it becomes the provider and assumes ALL provider obligations. Under DORA Articles 28-44, contractual arrangements with third-party ICT providers must include: clear description of services, availability and integrity provisions, exit strategies and transition plans. Critical third-party providers are subject to Union oversight. When combined: the financial entity must maintain DORA third-party oversight AND ensure the AI system meets AI Act high-risk requirements, with the risk that any modification to fit the financial use case could trigger full provider status under the AI Act.",
        "predicted_answer": "Under the EU AI Act, a financial institution deploying a third-party high-risk AI system must comply with Article 16 obligations for providers and Article 26 obligations for deployers simultaneously, meaning the institution cannot transfer compliance liability to its vendor \u2014 it remains directly accountable for conformity assessments, technical documentation, and human oversight mechanisms. If the third-party system crosses into prohibited territory under Article 5 (for example, manipulative techniques or unlawful biometric categorisation), the financial institution faces immediate decommissioning obligations with no transition period and penalties reaching \u20ac35 million or 7% of global annual turnover. The AI Act's supply chain accountability framework intersects with DORA's ICT third-party risk requirements \u2014 which mandate contractual provisions, concentration risk assessments, and exit strategies for critical ICT providers \u2014 meaning the institution must simultaneously satisfy both regimes without one substituting for the other; however, the specific DORA article-level obligations governing that intersection require dedicated ICT-risk governance analysis that falls outside the validated scope of the expert input available here. What can be stated with confidence is that the AI Act obligations are not contingent on DORA compliance, and vice versa \u2014 both apply in parallel from their respective enforcement dates, with the AI Act's prohibited practices provisions having applied since August 2024 and high-risk system obligations phasing in through 2026. Any synthesis claiming full dual-framework compliance without verified DORA-specific analysis would exceed the validated evidence available.",
        "exact_match": 0.0,
        "f1": 0.6666666666666666,
        "latency_ms": 223650.50920000067,
        "cost_usd": 0.19305899999999898,
        "total_tokens": 193,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 209,
        "shacl_fail": 131,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B08",
        "question": "How do AI Act risk management requirements interact with DORA ICT risk management for credit scoring AI?",
        "gold_answer": "Credit scoring AI is classified as high-risk under Annex III point 5(b). Under AI Act Article 9, the provider must implement a continuous iterative risk management system throughout the system's lifecycle, covering known and foreseeable risks with residual risk kept to acceptable levels. Under DORA Articles 5-16, the financial entity must maintain an ICT risk management framework with strategies, policies, procedures and protocols. These two requirements must be integrated: the AI-specific risk management (bias, accuracy, fairness) must be embedded within the broader DORA ICT risk framework, with the AI risk management system feeding into DORA's systematic documentation and the DORA framework providing the organisational infrastructure for AI risk governance.",
        "predicted_answer": "The expert analysis provided addresses only AI Act Article 5 prohibited practices and explicitly falls outside the scope of the AI Act risk management and DORA ICT risk intersection you are asking about. Because no validated expert analysis covering AI Act Article 9 risk management requirements or DORA's ICT risk framework (Regulation (EU) 2022/2554, Articles 5\u201316) was returned with sufficient confidence, a direct synthesis of how these frameworks interact for credit scoring AI cannot be responsibly constructed from the available inputs. What can be confirmed from the in-scope analysis is that a credit scoring AI must first be assessed against AI Act Article 5 prohibited practices \u2014 particularly Article 5(1)(b) on exploiting vulnerabilities and Article 5(1)(c) on social scoring \u2014 before any risk management layering question becomes relevant, since a prohibited system cannot be legitimized through compliance with Article 9 or DORA obligations. For the specific interaction between AI Act Article 9 continuous risk management obligations and DORA's ICT risk management requirements as they apply simultaneously to financial entities deploying credit scoring AI, you should consult governance expertise specifically covering high-risk AI systems under AI Act Annex III and DORA's financial sector ICT obligations, as that analysis was not available in the validated outputs provided here.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 181936.02029999965,
        "cost_usd": 0.15883799999999848,
        "total_tokens": 177,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 223,
        "shacl_fail": 137,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B09",
        "question": "What dual incident notification requirements apply when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2?",
        "gold_answer": "Under AI Act Article 73, the provider must report the serious incident to market surveillance authorities within 15 days of establishing a causal link. Under NIS2 Article 23, the essential entity must notify the CSIRT: within 24 hours (early warning), within 72 hours (incident notification with severity assessment), and within one month (final report with root cause). The NIS2 timelines are much more aggressive than the AI Act's 15-day deadline. Both notifications go to different authorities (market surveillance vs. CSIRT) with different templates and different information requirements. The entity must coordinate both reporting streams simultaneously.",
        "predicted_answer": "The available expert analysis cannot support a complete answer to this query. The only validated input addresses whether the AI system employs a prohibited practice under AI Act Article 5, which is a threshold question distinct from notification obligations. No validated expert output covering AI Act incident reporting provisions (such as those applicable to high-risk systems under Title IV) or NIS2 dual-notification timelines and thresholds was provided for synthesis.\n\nAnswering the specific question about simultaneous notification obligations \u2014 including NIS2's 24-hour early warning and 72-hour incident notification requirements for operators of essential services, and how those interact with any AI Act serious incident reporting duties \u2014 would require drawing on governance domains not represented in the validated analyses above. Doing so would risk presenting unverified compliance guidance as authoritative, which the governance constraints here prohibit. You should consult experts specifically scoped to NIS2 Article 19\u201323 obligations and AI Act Title IV/Title IX incident reporting before acting on any dual-notification framework.",
        "exact_match": 0.0,
        "f1": 0.21008403361344538,
        "latency_ms": 155238.59489999813,
        "cost_usd": 0.12100800000000156,
        "total_tokens": 173,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 243,
        "shacl_fail": 137,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B10",
        "question": "How does the AI Act's conformity assessment interact with existing product safety legislation for AI-embedded products?",
        "gold_answer": "Under Article 6(1), AI systems that are safety components of products covered by Annex I Section A harmonisation legislation are subject to the same conformity assessment as the parent legislation. Under Article 43, for stand-alone high-risk AI in Annex III, providers may use internal control (Annex VI) or notified body assessment (Annex VII). For Annex I products, AI Act requirements are integrated INTO the existing product conformity assessment \u2014 not a separate process. The CE marking (Art. 48) covers both the product safety and AI compliance. Any substantial modification triggers a new conformity assessment covering both dimensions.",
        "predicted_answer": "The expert analysis provided covers only Art. 5 prohibited practices and explicitly falls outside the scope of conformity assessment (Art. 40-49) and existing product safety legislation interactions (Art. 83), meaning a fully validated synthesis on the core query cannot be responsibly constructed from the available evidence. What can be stated with confidence is that where an AI-embedded product deploys practices prohibited under Art. 5(a)-(g) \u2014 such as subliminal manipulation, social scoring, or real-time biometric categorisation \u2014 those violations trigger immediate decommissioning obligations irrespective of any conformity assessment status, with penalties reaching up to EUR 35 million or 7% of global annual turnover. The interaction between Art. 40-49 conformity procedures and sector-specific product safety regimes such as the Machinery Regulation or the Medical Devices Regulation falls outside the validated scope of the available expert input and should not be characterised here without authoritative analysis of those provisions. A complete answer to this query requires expert input specifically covering Art. 83 (existing Union harmonisation legislation), Art. 40-49 (conformity assessment procedures), and the relevant sectoral safety frameworks operating simultaneously alongside the AI Act.",
        "exact_match": 0.0,
        "f1": 0.22656249999999997,
        "latency_ms": 249244.5198000023,
        "cost_usd": 0.22692600000000152,
        "total_tokens": 168,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 245,
        "shacl_fail": 155,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-C01",
        "question": "A bank deploys a credit scoring AI system that experiences a data breach affecting 50,000 customers. Map out ALL regulatory obligations, timelines, authorities, and potential penalties across every applicable framework.",
        "gold_answer": "This triggers obligations under four frameworks simultaneously: (1) AI Act: Report serious AI incident to market surveillance within 15 days (Art. 73). Potential penalty: EUR 15M or 3% turnover for Chapter III violations (Art. 99). (2) GDPR: Notify supervisory authority within 72 hours and affected data subjects without undue delay (Art. 33-34). Penalty: up to EUR 20M or 4% turnover (Art. 83). (3) DORA: Report major ICT incident to financial supervisory authority with classification by impact (Art. 17-23). Penalty: per national implementation. (4) NIS2: If the bank is an essential entity, notify CSIRT within 24 hours (early warning) and 72 hours (full notification) (Art. 23). Penalty: up to EUR 10M or 2% turnover (Art. 34). Maximum cumulative penalty exposure: up to 9% of global turnover (3% AI Act + 4% GDPR + 2% NIS2). Four different authorities must be notified with four different timelines and templates.",
        "predicted_answer": "The credit scoring AI data breach triggers simultaneous obligations under at least three frameworks, but the synthesized expert input only provides validated coverage of EU AI Act Article 5 prohibited practices \u2014 the GDPR breach notification timelines (Article 33's 72-hour supervisory authority notification and Article 34's individual notification requirements), financial sector obligations under EBA guidelines, and cybersecurity frameworks like NIS2 were explicitly flagged as outside the assessed domain, meaning any specific timelines, penalty figures, or authority mappings for those frameworks cannot be responsibly stated here without validated expert input.\n\nWhat can be stated with confidence is that the AI Act Article 5 assessment is relevant only if the credit scoring system uses manipulative techniques (Art. 5(a)), exploits vulnerable groups (Art. 5(b)), or constitutes prohibited social scoring by public authorities (Art. 5(c)) \u2014 and if any of those violations are confirmed, penalties reach up to \u20ac35 million or 7% of global annual turnover under Article 99, but the breach itself does not automatically trigger Article 5 liability, as Article 5 concerns the design and deployment practices of the AI system rather than its security posture.\n\nThe honest synthesis is that this query requires validated outputs from GDPR supervisory authority experts, financial regulation experts, and cybersecurity compliance experts before a complete regulatory map can be responsibly constructed \u2014 presenting fabricated timelines or penalty figures for GDPR Article 33, PSD2, or NIS2 as if they were synthesized from expert analysis would misrepresent the actual state of the available validated inputs and could cause material compliance harm to the institution relying on this assessment.",
        "exact_match": 0.0,
        "f1": 0.25257731958762886,
        "latency_ms": 221736.5471999983,
        "cost_usd": 0.21859800000000007,
        "total_tokens": 195,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 254,
        "shacl_fail": 166,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-C02",
        "question": "An energy utility deploys AI for grid management (critical infrastructure). A third-party provider substantially modifies the model. Who bears which obligations under the AI Act, DORA, and NIS2?",
        "gold_answer": "Under AI Act Article 25, the third party that substantially modifies the system becomes the provider and assumes ALL provider obligations: risk management (Art. 9), data governance (Art. 10), documentation (Art. 11), conformity assessment (Art. 43), CE marking (Art. 48). The energy utility remains the deployer with Art. 26 obligations: human oversight, monitoring, incident reporting. Under NIS2, the energy utility as essential entity must maintain cybersecurity measures (Art. 21) and incident notification (Art. 23) regardless of who is the AI provider. Under DORA (if applicable as financial-adjacent), third-party management (Art. 28-44) requires contractual arrangements covering the modification scope. Critical infrastructure AI is high-risk under Annex III point 2, so ALL Chapter III requirements apply. The modification creates a liability gap: the original provider is no longer responsible, but the modifier may not have the original's technical infrastructure.",
        "predicted_answer": "The query cannot be fully resolved from the validated expert outputs provided, as the available analysis is scoped exclusively to AI Act Article 5 prohibited practices and explicitly declines to address the core question of obligation allocation. The Article 5 agent confirms no prohibited practices analysis is triggered here, but the critical provisions governing this scenario \u2014 AI Act Article 25 (provider/deployer responsibility when a third party substantially modifies a high-risk system, which would reclassify that third party as a new provider), DORA Articles 28\u201330 (ICT third-party risk management obligations for financial and energy entities), and NIS2 Article 21 (security obligations for operators of essential services including energy infrastructure) \u2014 were not covered by any validated expert output. Synthesizing a specific answer on obligation allocation, penalty exposure (e.g., AI Act fines up to \u20ac30M or 6% global turnover, DORA contractual and supervisory requirements, NIS2 incident reporting within 24/72 hours) or how these three frameworks interact simultaneously would require me to generate unvalidated legal analysis, which the governance constraints of this synthesis task prohibit. To answer this question responsibly, you should obtain validated outputs from agents covering AI Act Articles 6, 25, and 28, DORA Articles 28\u201330, and NIS2 Article 21 before synthesis is attempted.",
        "exact_match": 0.0,
        "f1": 0.277602523659306,
        "latency_ms": 207927.09699999978,
        "cost_usd": 0.1966019999999995,
        "total_tokens": 188,
        "convergence_rounds": 2,
        "active_nodes": 10,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 264,
        "shacl_fail": 176,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0
      },
      {
        "question_id": "MG-C03",
        "question": "Design a comprehensive compliance program for a hospital deploying an AI diagnostic system, addressing the AI Act, GDPR, NIS2, and fundamental rights. What are the key tensions between these frameworks?",
        "gold_answer": "The hospital must: (1) AI Act: The diagnostic AI is high-risk (Annex III \u2014 health). Ensure provider has completed conformity assessment (Art. 43), implement human oversight with qualified medical staff (Art. 14), monitor system operation (Art. 26), report incidents (Art. 73). (2) GDPR: Conduct DPIA (Art. 35) for health data processing, ensure lawful basis (likely explicit consent or vital interests), comply with data minimisation while ensuring sufficient training data quality. (3) NIS2: Hospital as essential entity must maintain cybersecurity framework (Art. 21), report AI-related security incidents to CSIRT (Art. 23). (4) Fundamental Rights: Conduct FRIA (Art. 27) assessing discrimination risks in diagnostic outcomes. Key tensions: GDPR data minimisation vs AI Act data quality requirements (Art. 10); human oversight (Art. 14) vs clinical workflow efficiency; transparency (Art. 13) vs medical liability if clinicians over-rely on explained AI outputs; FRIA/DPIA duplication burden. Resolution: combine FRIA and DPIA, establish clinical AI governance committee, implement tiered human oversight based on diagnostic confidence levels.",
        "predicted_answer": "The hospital's AI diagnostic system triggers simultaneous obligations under at least four frameworks, creating compounding compliance burdens that must be addressed in an integrated program rather than sequentially. Under the AI Act, the system qualifies as high-risk under Annex III (medical devices/diagnostics), requiring conformity assessments, technical documentation, human oversight mechanisms, and transparency obligations under Articles 13\u201317 before deployment, while Article 5 prohibitions on manipulative techniques and emotion recognition must be audited at the outset with decommissioning and penalties up to \u20ac35 million for violations. GDPR Articles 9 and 22 apply simultaneously because diagnostic AI processes special category health data and may constitute automated decision-making with legal or similarly significant effects on patients, requiring explicit consent or an Article 9(2)(h) healthcare exemption, a Data Protection Impact Assessment under Article 35, and meaningful human review \u2014 a requirement that directly tensions with AI Act Article 13's transparency mandate, since full algorithmic explainability may conflict with intellectual property protections the vendor invokes. NIS2 Directive obligations apply because hospitals are classified as essential entities under Annex I, requiring incident reporting within 24 hours of awareness and full notification within 72 hours, supply chain security assessments of the AI vendor, and risk management measures under Articles 21 and 23 \u2014 yet the AI Act's conformity documentation requirements may demand vendor transparency that NIS2's security-by-design principles counsel against disclosing broadly. The fundamental rights tension is most acute at the intersection of GDPR Article 22's right not to be subject to solely automated decisions and the clinical reality that AI diagnostic outputs may functionally determine treatment pathways, meaning the hospital cannot satisfy fundamental rights obligations through nominal human oversight that is procedurally present but substantively hollow, and must document genuine clinical override capacity to withstand scrutiny under both the EU Charter Article 8 and forthcoming AI Act enforcement by national market surveillance authorities.",
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      {
        "question_id": "MG-C04",
        "question": "What is the total maximum fine exposure for a tech company that develops a GPAI model with systemic risk, violates prohibited AI practices, fails GDPR data protection, and breaches NIS2 cybersecurity?",
        "gold_answer": "Maximum cumulative fine exposure across all frameworks: AI Act prohibited practices (Art. 5/99): EUR 35M or 7% of worldwide turnover. AI Act GPAI systemic risk violations (Art. 51-55/99): EUR 15M or 3% of turnover. GDPR basic principles violation (Art. 83): EUR 20M or 4% of turnover. NIS2 essential entity (Art. 34): EUR 10M or 2% of turnover. Total maximum: 16% of worldwide annual turnover (7% + 3% + 4% + 2%), or EUR 80 million (35M + 15M + 20M + 10M), whichever is higher for each individual fine. Each fine is assessed independently by different authorities under different frameworks. The ne bis in idem principle may limit overlap within the same jurisdiction, but cross-framework fines from different regulatory regimes are generally cumulative.",
        "predicted_answer": "Based on the confirmed expert analysis and the referenced regulatory frameworks, a tech company facing this combined exposure would confront simultaneous liability across four distinct legal instruments. Under EU AI Act Article 5, prohibited AI practice violations carry up to EUR 35 million or 7% of worldwide annual turnover (whichever is higher), enforceable from 2 February 2025. GPAI systemic risk violations under Articles 51\u201355 of the EU AI Act carry up to EUR 15 million or 3% of worldwide annual turnover. GDPR infringements of core data protection obligations under Article 83(4)\u2013(5) add up to EUR 10 million or 2% of global turnover, or up to EUR 20 million or 4% of global turnover for the most serious violations respectively. NIS2 Directive Article 34 adds up to EUR 10 million or 2% of global annual turnover for essential entities. Critically, these frameworks apply **simultaneously and independently** \u2014 each regulator (national AI authority, data protection authority, and cybersecurity authority) may pursue enforcement concurrently without offset, meaning theoretical maximum cumulative exposure could exceed **16% of worldwide annual turnover**, though it must be noted that the confirmed expert input only validated the Article 5 figure at 92% confidence, and the remaining penalty figures require validation from their respective specialized domains before any definitive legal reliance should be placed on this aggregated calculation.",
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      {
        "question_id": "MG-C05",
        "question": "How should a multinational deploy an AI recruitment system across the EU, considering the AI Act's territorial scope, GDPR's cross-border processing rules, and varying NIS2 transposition?",
        "gold_answer": "AI Act Article 2 applies to all deployers located in the Union, regardless of provider location. Recruitment AI is high-risk under Annex III point 4. The deployer must: ensure conformity assessment from the provider (Art. 43), implement human oversight per Art. 14 (qualified HR staff in each jurisdiction), and conduct FRIA (Art. 27) for each country's specific population. GDPR requires: identifying a lead supervisory authority for cross-border processing, conducting DPIAs per Art. 35, ensuring lawful basis (likely legitimate interest or consent), and respecting Art. 22 automated decision-making rights in each jurisdiction. NIS2 transposition varies by Member State \u2014 the company must verify whether it is classified as essential/important entity in each country where it operates. Key challenge: a recruitment decision valid under one Member State's GDPR interpretation may violate another's, and the AI Act's uniform high-risk requirements may conflict with varying national employment laws.",
        "predicted_answer": "The AI Act's prohibited practices provisions (Art. 5(f), effective 2 February 2025) bar any emotion recognition functionality in the recruitment system across all EU deployments, regardless of where the deploying entity is established, with penalties reaching EUR 35 million or 7% of global turnover. However, the expert analysis provided covers only Art. 5 prohibited practices, and the query's additional dimensions \u2014 specifically the AI Act's territorial scope for high-risk recruitment systems under Annex III, GDPR's cross-border processing mechanisms under Art. 46\u201349 and the lead supervisory authority rules under Art. 56, and the varying national transpositions of NIS2 \u2014 were not addressed by the validated expert inputs available. A compliant deployment architecture therefore cannot be fully specified from the evidence provided: the prohibition on emotion recognition and manipulative screening (Art. 5(a)) can be stated with confidence, but determinations about lawful cross-border data flows, which Member State's DPA holds lead authority, and which NIS2 obligations apply in each jurisdiction require analysis that falls outside what was validated here. Proceeding without that additional expert input would risk non-compliance across multiple simultaneous regulatory frameworks, each carrying independent enforcement consequences.",
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      {
        "question_id": "MG-C06",
        "question": "A law enforcement agency wants to use real-time biometric identification in a public space to prevent an imminent terrorist threat. What is the complete legal pathway, conditions, and oversight requirements across all applicable frameworks?",
        "gold_answer": "AI Act Article 5 generally prohibits real-time remote biometric identification in public spaces for law enforcement. Exception: strictly necessary for prevention of specific, substantial and imminent threat to life or genuine terrorist threat. Conditions: must be authorised by judicial authority or independent administrative authority; limited in time, geographic and personal scope; subject to prior fundamental rights impact assessment; registered in the EU database. Article 14 requires verification by at least two natural persons. Biometric identification is high-risk Annex III point 1 \u2014 full Chapter III requirements apply. GDPR applies: processing of biometric data (special category, Art. 9) requires explicit legal basis, likely Art. 9(2)(g) substantial public interest. GDPR Art. 35 DPIA required before deployment. If the system is embedded in essential infrastructure, NIS2 Art. 21 cybersecurity measures apply. The complete chain: judicial authorisation \u2192 FRIA \u2192 DPIA \u2192 conformity verified \u2192 human oversight (2 persons) \u2192 time-limited deployment \u2192 incident reporting to multiple authorities.",
        "predicted_answer": "Under the EU AI Act Art. 5(1)(d), real-time biometric identification in public spaces for an imminent terrorist threat is exceptionally permitted but requires a genuine, credible threat and prior judicial or administrative authorization before deployment, with use strictly limited to what is necessary and proportionate to the specific threat. This exception operates simultaneously with GDPR Art. 9, which governs the processing of biometric data as a special category requiring additional legal basis and safeguards, and with EU Charter Arts. 1, 7, and 8, which impose cumulative fundamental rights obligations that cannot be displaced even in security contexts. Compliance obligations under the AI Act became enforceable as of 2 February 2025 with no transition period for prohibited or high-risk use violations, meaning unauthorized or disproportionate deployment exposes the agency to penalties of up to EUR 35 million or 7% of global annual turnover. It would be inaccurate to characterize any deployment as compliant without documented evidence of prior authorization, a verified threat assessment, and an immediate post-deployment audit; the legal pathway is narrow and procedurally demanding, not a general security carve-out.",
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        "total_tokens": 170,
        "convergence_rounds": 2,
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      {
        "question_id": "MG-C07",
        "question": "An insurance company uses AI for risk assessment and a data protection authority finds the training data was biased against certain ethnic groups. Trace the full enforcement cascade across AI Act, GDPR, and potential liability.",
        "gold_answer": "Insurance risk assessment AI is high-risk under Annex III point 5 (essential services). Enforcement cascade: (1) AI Act Art. 10 violation \u2014 data governance failed to detect and mitigate bias in training data. Art. 9 risk management failed to identify discrimination risk. Penalties: EUR 15M or 3% turnover. (2) GDPR Art. 5 violation \u2014 fairness principle breached through discriminatory processing. Art. 22 \u2014 automated decisions with significant effects lacked adequate safeguards. Penalties: EUR 20M or 4% turnover. (3) Fundamental Rights \u2014 Art. 27 FRIA should have identified discrimination risk against ethnic groups, linked to EU Charter Art. 21 non-discrimination. (4) Product Liability \u2014 affected individuals may bring civil claims for discriminatory insurance pricing. (5) Corrective cascade: market surveillance authority can order system withdrawal (AI Act), data protection authority can order processing halt (GDPR), and the provider must conduct new conformity assessment after fixing the bias. Combined exposure: 7% turnover (3%+4%) plus civil liability.",
        "predicted_answer": "The enforcement cascade operates simultaneously across three frameworks without sequential ordering. Under the AI Act, ethnic bias in insurance risk assessment likely triggers Art. 5(g) (biometric categorisation inferring sensitive characteristics) and potentially Art. 5(a) (techniques causing harm), exposing the insurer to penalties of up to EUR 35 million or 7% of global annual turnover under Art. 99(3), with these prohibitions enforceable from 2 February 2025 and carrying no remediation or transition period \u2014 the obligation is immediate decommissioning. Concurrently, GDPR Art. 9 is engaged because ethnic origin constitutes special category data whose processing requires explicit legal basis, while Art. 22 grants affected individuals rights against solely automated decisions producing significant effects, meaning each risk assessment decision issued without human review compounds the violation independently. The two frameworks interact such that a GDPR supervisory authority finding under Art. 9 or Art. 22 can serve as predicate evidence for the AI Act market surveillance authority's Art. 5 determination, and vice versa, creating compounding rather than alternative penalty exposure \u2014 though whether regulators would stack maximum penalties or coordinate to avoid disproportionality remains an open enforcement question that no compliance posture can currently resolve with certainty. Downstream civil liability under national tort law or insurance sector regulation may additionally attach to individual claimants who can demonstrate discriminatory premium or coverage decisions flowed from the biased model, with Charter Art. 1 (human dignity) potentially informing judicial interpretation of damages in Member State proceedings.",
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      {
        "question_id": "MG-C08",
        "question": "How does the AI Act's quality management system requirement interact with DORA's ICT governance and NIS2's cybersecurity policies for a fintech company running AI-based fraud detection?",
        "gold_answer": "The fintech faces three overlapping governance frameworks: AI Act Art. 17 QMS: requires documented strategies for regulatory compliance, design procedures, quality control, testing, data management, risk management, post-market monitoring, incident reporting, and resource management. DORA ICT Governance (Art. 5-16): requires internal governance framework for ICT risk with strategies, policies, procedures, and protocols covering all ICT assets. NIS2 Art. 21: requires policies on risk analysis, incident handling, business continuity, supply chain security, and cybersecurity training. Integration approach: the QMS should be the umbrella system incorporating both DORA ICT risk management and NIS2 cybersecurity measures as sub-frameworks. The AI-specific risk management (bias, accuracy, fairness for fraud detection) feeds into the broader ICT risk assessment, while NIS2 cybersecurity requirements map to AI Act Art. 15 (accuracy, robustness, cybersecurity). Duplication risk: without integration, the fintech maintains three separate but overlapping governance systems.",
        "predicted_answer": "The queried expert (AI Act Art. 5) correctly identified it lacks jurisdiction over this interaction, and no HIGH_RISK_AI or CROSS_FRAMEWORK expert output was provided with validated findings on Art. 17 quality management systems, DORA ICT governance requirements, or NIS2 cybersecurity policy obligations. Without confident, validated expert analyses covering AI Act Art. 17 (quality management for high-risk AI systems, which AI-based fraud detection likely triggers given its classification under Annex III), DORA Art. 6 ICT risk management frameworks, and NIS2 Art. 21 cybersecurity measures, any synthesis of how these three frameworks interact simultaneously would constitute fabrication rather than analysis. A fintech deploying AI fraud detection faces genuine concurrent obligations across all three regimes \u2014 including potential AI Act penalties of up to \u20ac15 million or 3% of global turnover, DORA compliance deadlines of January 2025, and NIS2 enforcement active since October 2024 \u2014 but the specific interaction mechanics, overlap in documentation requirements, and potential compliance efficiencies cannot be responsibly stated here without the relevant expert outputs. You should resubmit this query with HIGH_RISK_AI and CROSS_FRAMEWORK agents included in the expert panel.",
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      {
        "question_id": "MG-C09",
        "question": "Compare and contrast the incident reporting timelines, authorities, and templates across AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank.",
        "gold_answer": "For a single AI incident affecting a critical infrastructure bank, four parallel reporting obligations trigger: AI Act (Art. 73): Report to market surveillance authority within 15 days of establishing causal link. Content: AI system details, nature of incident, corrective measures. GDPR (Art. 33): Notify data protection authority within 72 hours of becoming aware. Content: nature of breach, categories of data subjects, likely consequences, mitigation measures. DORA (Art. 17-23): Report to financial supervisory authority per incident classification. Content: standardised template with clients affected, duration, geographic spread, data loss. NIS2 (Art. 23): Notify CSIRT within 24 hours (early warning), 72 hours (full notification), one month (final report). Content: suspected malicious cause, severity, impact, root cause. Comparison: NIS2 has fastest deadline (24h), GDPR next (72h), AI Act slowest (15 days). Four different authorities, four different templates, four different legal bases. The bank's incident response team needs a unified procedure mapping one event to four streams.",
        "predicted_answer": "The available expert analysis covers only the AI Act Article 5 prohibited practices dimension, and the other framework-specific agents (GDPR, DORA, NIS2) did not return validated outputs for this query \u2014 meaning a fully synthesized cross-framework comparison cannot be responsibly constructed from the evidence provided. What can be stated with confidence is this: if the AI system involved in the incident constitutes a prohibited practice under AI Act Article 5 \u2014 such as manipulative AI, real-time biometric identification in public spaces, or emotion recognition in prohibited contexts \u2014 the bank faces immediate decommissioning obligations with no transition period and penalties up to \u20ac35 million or 7% of global annual turnover, and this obligation runs concurrently with, not instead of, whatever timelines GDPR Article 33 (72-hour personal data breach notification to supervisory authority), DORA Article 19 (initial notification of major ICT-related incidents, with timelines set by regulatory technical standards under Article 20), and NIS2 Article 23 (24-hour early warning, 72-hour incident notification, one-month final report) would independently impose. The interaction between these frameworks is additive and not mutually exclusive \u2014 a single AI incident at a critical infrastructure bank can simultaneously trigger all four regimes \u2014 but the specific timelines, competent authorities, and reporting templates for GDPR, DORA, and NIS2 require validated specialist analysis that was not returned here and cannot be responsibly fabricated to complete this synthesis.",
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      {
        "question_id": "MG-C10",
        "question": "An AI regulatory sandbox participant wants to test a novel emotion recognition system in a workplace setting. What are the legal boundaries, exceptions, and framework interactions?",
        "gold_answer": "This faces a direct prohibition: AI Act Article 5 prohibits emotion recognition in workplace and educational institutions. Regulatory sandboxes (Art. 57-58) provide controlled testing environments, but Article 5 prohibitions are absolute \u2014 no sandbox exception. The only permitted emotion recognition is outside workplace/education contexts. If the participant proposes a modified scope (e.g., public safety context), then: AI Act: must operate within sandbox plan agreed with competent authority; GDPR: emotion recognition processes biometric data (special category, Art. 9) requiring explicit consent or substantial public interest; GDPR Art. 35 DPIA mandatory; fundamental rights assessment required for any biometric categorisation. Even in a sandbox, GDPR protections apply fully \u2014 sandbox does not exempt from data protection law. NIS2 applies if the sandbox operator is an essential entity. Bottom line: workplace emotion recognition cannot be tested even in a sandbox. The prohibition is not a regulatory barrier that sandboxes can bypass \u2014 it's a fundamental rights protection.",
        "predicted_answer": "The proposed emotion recognition system in a workplace setting is absolutely prohibited under EU AI Act Art. 5(1)(f), which admits no derogations and has been enforceable since 2 February 2025. Regulatory sandboxes established under Art. 57\u201363 cannot authorize this deployment, as they are designed exclusively to facilitate testing of lawful AI systems and carry no power to override Art. 5 prohibitions. Simultaneously, GDPR Art. 9 applies because emotional and biometric data constitute special-category personal data, compounding the legal barrier with an independent prohibition on processing absent an explicit lawful basis \u2014 which cannot be established where the underlying AI application is itself banned. These frameworks interact cumulatively rather than alternatively: a violation triggers both AI Act penalties of up to \u20ac35 million or 7% of global annual turnover and potential GDPR enforcement, while Charter Art. 1 on human dignity provides the constitutional foundation underpinning both regimes. There is no compliant pathway for this use case under current law.",
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}