{
  "single-agent": {
    "summary": {
      "method": "single-agent",
      "dataset": "MultiGov-30-v2",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.4378842856197886,
      "avg_latency_ms": 3921.9592432957143,
      "avg_cost_usd": 0.0013168700000000004,
      "avg_tokens": 13168.7,
      "avg_rounds": 1.0,
      "avg_nodes": 1.0,
      "total_cost_usd": 0.03950610000000001,
      "total_latency_s": 117.65877729887143,
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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": "The EU AI Act prohibits certain AI practices that are deemed high-risk and pose significant risks to fundamental rights, public health, safety, or democratic values. These prohibited practices include:\n\n1. **Biometric Identification and Categorisation**: AI systems that identify or categorize individuals based on biometric data without their explicit consent, unless the system is used for law enforcement purposes and meets strict conditions.\n\n2. **Management and Operation of Critical Infrastructure**: AI systems that manage or operate critical infrastructure, such as energy, transport, health, and digital infrastructure, without meeting the high-risk requirements of the AI Act.\n\n3. **Employment, Workers Management, and Access to Self-Employment**: AI systems that make automated decisions concerning employment, workers' management, or access to self-employment without providing meaningful human oversight, unless the system is used for law enforcement purposes and meets strict conditions.\n\nThe penalties for these prohibited practices are severe:\n\n- **Prohibited Practice**: Fines of up to 6% of the total worldwide annual turnover of the AI system's parent entity.\n- **High-Risk Violation**: Fines of up to 3% of the total worldwide annual turnover of the AI system's parent entity.\n\nThese penalties are designed to ensure that the high-risk AI practices are",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 4023.203100077808,
        "cost_usd": 0.0013171,
        "total_tokens": 13171,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 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": "To place a high-risk AI system on the EU market, a provider must follow a comprehensive compliance chain that includes several key steps:\n\n1. **Conformity Assessment**: The AI system must be assessed to ensure it meets the requirements of the AI Act (Chapter III, Section 2). This involves either self-assessment or obtaining a certificate from a notified body for third-party assessments.\n\n2. **EU Declaration of Conformity**: A document must be drafted and signed by an authorized person, stating that the provider takes responsibility for the conformity of the AI system. This declaration must include details such as the provider's identity, the system's name and description, applicable harmonized standards, and details of the notified body (if applicable).\n\n3. **Registration in the EU Database**: The AI system must be registered in the EU database for high-risk AI systems. This registration is publicly accessible and contains information such as the system's description, intended purpose, conformity status, and the Member States where the system is placed on the market.\n\n4. **Affixing the CE Marking**: The CE marking must be visibly, legibly, and indelibly affixed to the high-risk AI system or its packaging/documents. This marking indicates that the AI system conforms with the requirements of",
        "exact_match": 0.0,
        "f1": 0.6,
        "latency_ms": 4024.3317000567913,
        "cost_usd": 0.0013181,
        "total_tokens": 13181,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 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": "Deployers of high-risk AI systems have specific obligations regarding human oversight, as outlined in the AI Act. These obligations are detailed in Annex III, Section 5, which specifies the requirements for high-risk AI systems. The key obligations for human oversight include:\n\n1. **Human Oversight**: The deployer must ensure that the AI system is subject to human oversight. This means that there should be human intervention or review in the decision-making process, especially when the AI system makes decisions that have significant impacts on individuals or where the AI system is used in critical infrastructure.\n\n2. **Appeal Processes**: The deployer must establish and implement appeal processes for the decisions made by the AI system. This ensures that individuals have a right to challenge the decisions made by the AI system and have their concerns addressed.\n\n3. **Human Review and Decision-Making**: In cases where the AI system's decisions are used in critical infrastructure or have significant impacts on individuals, the deployer must ensure that there is human review and decision-making. This means that the human oversight should not just be limited to the AI system's output, but also should be involved in the decision-making process itself.\n\nThese obligations are designed to ensure that the use of high-risk AI systems does not lead to decisions that are opaque or",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 3987.4929999932647,
        "cost_usd": 0.001319,
        "total_tokens": 13190,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 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": "The EU AI Act imposes specific requirements on general-purpose AI models with systemic risk, which are known as General Purpose AI (GPAI) models. These models are defined in the Act as those that are capable of performing a wide range of tasks and have the potential to be used across multiple sectors or industries. The key requirements for GPAI models include:\n\n1. **Model Card and Documentation**: The GPAI model provider must provide a model card, which is a document that includes information about the model, such as its architecture, training data, and any significant limitations or risks. The model card must also indicate whether the model is capable of performing tasks that are considered high-risk under the AI Act, such as biometric identification, management of critical infrastructure, or employment decisions.\n\n2. **Copyright Compliance**: The GPAI model provider must ensure that the model is developed and used in compliance with copyright law. This includes obtaining necessary permissions for the use of copyrighted material in training the model and ensuring that the model does not infringe on any existing copyrights.\n\n3. **Training Data Summary**: The GPAI model provider must provide a summary of the training data used to develop the model. This summary should include details about the types of data used, the sources of the data, and any",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 4027.456699870527,
        "cost_usd": 0.0013194,
        "total_tokens": 13194,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 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": "The General Data Protection Regulation (GDPR) outlines several principles for processing personal data, which are designed to protect the privacy and rights of individuals. These principles include:\n\n1. **Lawfulness, Fairness, and Transparency**: Personal data should be processed lawfully, fairly, and in a transparent manner.\n2. **Purpose Limitation**: Data should be collected for specified, explicit, and legitimate purposes and not further processed in a manner incompatible with those purposes.\n3. **Data Minimization**: Collect only the data that is necessary for the purposes of processing.\n4. **Accuracy**: Ensure that personal data is accurate and, where necessary, kept up to date.\n5. **Storage Limitation**: Personal data should be kept in a form that permits identification of data subjects for no longer than is necessary for the purposes of processing.\n6. **Integrity and Confidentiality**: Ensure that personal data is processed in a manner that ensures appropriate security of the personal data, including protection against unauthorized or unlawful processing and against accidental loss, destruction, or damage.\n\nWhen it comes to AI training, these principles apply in the following ways:\n\n- **Purpose Limitation**: The AI training process should be designed to achieve specific objectives, such as improving model accuracy or efficiency, and should not be used",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 4006.7630000412464,
        "cost_usd": 0.0013178,
        "total_tokens": 13178,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 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": "For AI systems that generate deepfakes or synthetic content, there are specific transparency obligations under the AI Act. These obligations are outlined in Article 13 of the AI Act, which requires that the AI system provides information about the generation of synthetic content. Specifically, the system must:\n\n1. **Identification of Synthetic Content**: The AI system should be able to identify and label the synthetic content it generates, distinguishing it from real content.\n\n2. **Explanation of Generation Process**: The system should provide an explanation of the process used to generate synthetic content. This explanation should include details about the specific techniques and datasets used.\n\n3. **Metadata and Metadata Labeling**: The AI system must include metadata that labels the synthetic content as such. This metadata should be easily accessible and understandable to users.\n\n4. **User Interface Transparency**: The user interface of the AI system should be designed to clearly indicate when synthetic content is being used, and users should be able to distinguish synthetic content from real content.\n\nThese transparency obligations are designed to help users understand the nature of the content they are interacting with and to ensure that the use of synthetic content is transparent and accountable. The goal is to enhance user trust and to facilitate responsible use of synthetic content in AI systems.",
        "exact_match": 0.0,
        "f1": 0.4,
        "latency_ms": 3981.661499943584,
        "cost_usd": 0.0013189,
        "total_tokens": 13189,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 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": "DORA (Digital Operational Resilience Act) defines ICT risk management requirements for financial entities in several key ways:\n\n1. **Compliance with Regulations**: Financial entities must comply with the specific ICT risk management requirements outlined in DORA, such as those in Articles 5-7, 28-30, and 17-19. These articles cover areas like business continuity and crisis management, supply chain security, and incident reporting, respectively.\n\n2. **Risk Analysis**: Financial entities are required to conduct risk analyses to identify potential ICT risks that could impact their operations. This includes vetting AI model providers, verifying the provenance of training data, and assessing the security of open-source models.\n\n3. **Incident Handling**: They must have procedures in place to handle incidents, including AI-specific incident response procedures for adversarial attacks, data breaches, and AI system failures.\n\n4. **Supply Chain Security**: Financial entities must ensure that the AI systems they use have secure supply chains. This involves vetting AI model providers, verifying the training data's provenance, and assessing the security of open-source models.\n\n5. **Vulnerability Management**: They must include ML/AI vulnerabilities in their vulnerability scanning processes, such as using the OWASP ML Top",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 4006.2462999485433,
        "cost_usd": 0.0013155,
        "total_tokens": 13155,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 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": "NIS2 requires significant incidents to be notified to the CSIRT or competent authority within specific timelines:\n\n- An early warning notification must be given within 24 hours of becoming aware of the incident, indicating whether the incident is suspected of being caused by unlawful or malicious acts.\n- An incident notification must be provided within 72 hours after the early warning, updating the early warning and providing an initial assessment of severity and impact.\n- A final report must be submitted within one month after the incident notification, including a detailed description, root cause analysis, mitigation measures, and cross-border impact where applicable.\n\nThe CSIRT or competent authority must provide a response within 24 hours of receiving the early warning.",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 3157.882499974221,
        "cost_usd": 0.0013090000000000003,
        "total_tokens": 13090,
        "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-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": "Annex III defines high-risk AI use cases for employment and workers management in Category 4. Specifically, it covers:\n\n- Employment: AI systems used for determining eligibility for employment.\n- Workers management: AI systems used for managing workers or access to self-employment.\n- Access to self-employment: AI systems used for determining eligibility for self-employment.\n\nThese categories highlight the significant impact of AI in the employment sector, including eligibility determinations, worker management, and access to self-employment opportunities.",
        "exact_match": 0.0,
        "f1": 0.23529411764705885,
        "latency_ms": 2867.783599998802,
        "cost_usd": 0.0013050000000000002,
        "total_tokens": 13050,
        "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-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": "AI regulatory sandboxes under the EU AI Act are experimental environments designed to test and evaluate AI systems in a controlled setting. Their primary role is to facilitate innovation by allowing developers and deployers to experiment with AI technologies without immediate full compliance with all regulatory requirements. This can help in understanding the potential risks and benefits of AI applications before they are fully deployed.\n\nThe scope of AI regulatory sandboxes includes various aspects such as testing the technical and non-technical requirements of the AI Act, assessing the impact on fundamental rights, and evaluating the overall compliance with the Act. Sandboxes can be used to gather evidence for future regulatory decisions, provide feedback to developers, and help in developing best practices for AI deployment.\n\nKey points about AI regulatory sandboxes under the EU AI Act include:\n\n1. **Experimental Environment**: Sandboxes are designed to be experimental, allowing for the testing of AI systems in a controlled setting.\n2. **Testing Regulatory Requirements**: They are used to test and evaluate compliance with the technical and non-technical requirements outlined in the AI Act.\n3. **Impact Assessment**: Sandboxes help in assessing the impact of AI systems on fundamental rights and other relevant areas.\n4. **Evidence and Feedback**: They provide evidence for future regulatory decisions and offer feedback to developers and other stakeholders.\n5",
        "exact_match": 0.0,
        "f1": 0.21090909090909088,
        "latency_ms": 4028.407000005245,
        "cost_usd": 0.0013194,
        "total_tokens": 13194,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 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 jointly regulate automated decision-making that affects natural persons through a complementary approach. Here's how they work together:\n\n1. **GDPR Compliance**: The GDPR requires that any automated decision-making, including profiling, which produces legal effects concerning a natural person or similarly significantly affects them, is transparent and fair. This includes ensuring that individuals are informed about the decisions being made about them and have the right to challenge those decisions. The GDPR also mandates that individuals have the right to access the personal data used in the decision, to correct any inaccuracies, and to have automated decisions reviewed by a human.\n\n2. **AI Act Requirements**: The AI Act extends these requirements by imposing additional obligations on AI systems that are considered high-risk. For high-risk AI systems, the AI Act mandates a Fundamental Rights Impact Assessment (FRIA) to evaluate the impact on fundamental rights. It also requires a Fundamental Rights Impact Assessment (FRIA) for deployers of certain high-risk AI systems, including credit scoring and insurance risk assessment AI.\n\n3. **Joint Enforcement**: Both the AI Act and GDPR have enforcement mechanisms. Non-compliance with GDPR's requirements can lead to fines and other penalties, while non-compliance with the AI Act's requirements can also lead to fines and other penalties",
        "exact_match": 0.0,
        "f1": 0.326241134751773,
        "latency_ms": 3992.559999693185,
        "cost_usd": 0.0013186,
        "total_tokens": 13186,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 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": "The theoretical maximum combined penalty exposure for a high-risk AI system in an essential financial entity processing personal data, considering the cumulative penalties for the AI Act and GDPR, would be:\n\n- AI Act Art. 99: 7% of turnover (if prohibited practice, or 3% for high-risk violation)\n- GDPR Art. 83: 4% of turnover\n- DORA: financial supervisory penalty (varies, typically EUR 5M-20M)\n\nThe theoretical maximum combined penalty would be: 7% + 4% + DORA (typically EUR 5M-20M) = ~11% to ~16% of global annual turnover.\n\nHowever, actual enforcement would likely be lower due to proportionality, and the penalties would typically be applied separately rather than cumulatively. This theoretical maximum exposure is unique to the EU regulatory landscape.",
        "exact_match": 0.0,
        "f1": 0.7142857142857143,
        "latency_ms": 3476.133800111711,
        "cost_usd": 0.0013098,
        "total_tokens": 13098,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
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      },
      {
        "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": "A deployer using a high-risk AI system must comply with both the AI Act's fundamental rights impact assessment (FRIA) and GDPR's data protection impact assessment (DPIA). Here\u2019s how they must approach this:\n\n1. **FRIA (Fundamental Rights Impact Assessment):**\n   - The deployer must conduct a FRIA as mandated by the AI Act. This assessment evaluates the impact of the AI system on fundamental rights enshrined in the EU Charter of Fundamental Rights.\n   - The FRIA should be conducted BEFORE deployment, not after, to ensure that the deployer is fully aware of the potential rights impact and can take appropriate measures to mitigate any adverse effects.\n\n2. **DPIA (Data Protection Impact Assessment):**\n   - The deployer must also conduct a DPIA as required by GDPR. This assessment evaluates the privacy risks associated with the data processing by the AI system.\n   - The DPIA should be conducted BEFORE deployment as well, to ensure that the deployer is fully aware of the privacy risks and can take appropriate measures to mitigate any adverse effects.\n\n### Key Points to Note:\n\n- **Joint Conduct:** When both FRIA and DPIA are required (as is the case for high-risk AI systems processing personal data), the deployer",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 4018.567399587482,
        "cost_usd": 0.0013183000000000001,
        "total_tokens": 13183,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 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": "When a financial entity reports an AI-related incident under both the AI Act and DORA, they face dual obligations that require compliance with the specific requirements of both regulations. Specifically, the entity must ensure that the incident is reported to both the market surveillance authority (under the AI Act) and the competent authority (under DORA). This dual reporting ensures that all relevant regulatory bodies are informed about the incident, which is crucial for proper investigation and remediation.\n\nAdditionally, the entity must comply with the specific requirements of each regulation. For example, under the AI Act, the entity must ensure that the incident is reported \"immediately, not later than 15 days\" to the market surveillance authority. Under DORA, the entity must notify the CSIRT or competent authority within 24 hours of becoming aware of the incident. Therefore, the entity must coordinate these notifications and ensure that both authorities are promptly informed about the incident.\n\nFurthermore, the entity must ensure that the incident is reported to the CSIRT or competent authority within 72 hours under the AI Act, and to the CSIRT or competent authority under DORA. This dual reporting ensures that the incident is covered under both the AI Act and DORA, providing a comprehensive and timely response to the incident.\n\nIn summary,",
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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": "The AI Act and NIS2 both impose security requirements on entities that use AI systems, but they do so in different contexts and with different scopes. Here's how they interact, particularly in the context of high-risk AI systems used by essential entities:\n\n1. **Scope of Requirements**:\n   - **AI Act**: The AI Act requires high-risk AI systems to comply with specific cybersecurity measures (Art. 15) as part of its broader requirements for high-risk AI systems (Annex III). These measures are designed to ensure that high-risk AI systems are secure and do not pose undue risks to individuals or society.\n   - **NIS2**: The NIS2 (Network and Information Systems) regulation applies to essential entities, which are entities whose services are critical to the economy and society. NIS2 mandates cybersecurity measures for these entities to ensure they can withstand cyber threats and maintain essential services.\n\n2. **Interaction**:\n   - **High-Risk AI Systems**: High-risk AI systems, as defined in Annex III of the AI Act, fall under both the AI Act's and NIS2's purview. These systems are required to meet both sets of cybersecurity requirements. The AI Act's requirements are more specific to AI systems and their potential risks, while",
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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": "The GDPR's data protection by design principle and the AI Act's data governance requirements interact in a way that emphasizes the importance of embedding data protection considerations into the design and development of AI systems. Here's how they relate:\n\n1. **Data Protection by Design**: The GDPR requires that data protection measures be integrated into the design of systems from the outset. This principle is crucial for AI systems because it ensures that data protection is not an afterthought but a fundamental part of the system's architecture. For AI systems, this means that data protection measures should be integrated into the AI's training, inference, and decision-making processes to ensure that personal data is handled in a manner consistent with the principles of the GDPR.\n\n2. **Data Governance Requirements of the AI Act**: The AI Act includes specific data governance requirements, such as transparency about the use of AI, ensuring that AI systems are used in a fair and non-discriminatory manner, and conducting FRIAs (Fundamental Rights Impact Assessments) to evaluate the impact of AI systems on fundamental rights. These requirements align with the GDPR's data protection by design principle because they emphasize the importance of considering data protection from the very beginning of the AI system's lifecycle.\n\n3. **Overlap and Synergy**: The AI Act's data",
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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": "When a financial institution uses a third-party high-risk AI system, there are significant supply chain implications considering both the AI Act and DORA. Here are the key points:\n\n1. **AI Act and High-Risk AI Systems**: High-risk AI systems, as defined in Annex III of the AI Act, require specific compliance measures. If a financial institution is using a high-risk AI system, it must ensure that the system meets the requirements of the AI Act, including conformity assessment, EU declaration of conformity, and registration in the EU database. This means the financial institution must verify that the third-party AI system provider is compliant with these requirements.\n\n2. **DORA Requirements**: DORA (Digital Operational Resilience Act) imposes its own requirements on financial institutions, including third-party risk management, contractual safeguards, and exit strategies. If the financial institution is using a high-risk AI system, it must ensure that the third-party AI system provider complies with DORA requirements. This includes having contractual agreements in place that require the third-party AI system provider to provide access to audit the AI system, incident notification, data localization, and exit strategy.\n\n3. **Combining Requirements**: The combination of AI Act and DORA requirements creates a comprehensive supply chain governance framework. The financial institution",
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        "total_tokens": 13186,
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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 AI Act and DORA (Digital Operational Resilience Act) have overlapping requirements for credit scoring AI, particularly in the context of high-risk AI systems. Here's how they interact:\n\n1. **AI Act Requirements**: Credit scoring AI systems are categorized as high-risk AI systems under the AI Act's Annex III. This means they must comply with the high-risk requirements of the AI Act, including conformity assessment, CE marking, and the requirement to take responsibility for compliance (EU declaration of conformity). These requirements are designed to ensure the AI system is safe and secure, and that it meets the high-risk criteria set by the AI Act.\n\n2. **DORA Requirements**: Credit scoring AI systems also fall under the purview of DORA, which focuses on the digital operational resilience of financial institutions. DORA requires financial entities to manage third-party risks, conduct incident reporting, and ensure resilience testing. For credit scoring AI, this means the entity deploying the AI system must ensure that the AI system is integrated into their overall ICT risk management framework.\n\n3. **Interaction**: The interaction arises because both the AI Act and DORA require credit scoring AI systems to be robust and secure. The AI Act's high-risk requirements ensure the AI system itself is secure and meets the necessary criteria,",
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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": "When an AI system causing a cybersecurity breach affects essential infrastructure, the dual incident notification requirements under both the AI Act and NIS2 are as follows:\n\n- **NIS2**: The entity must notify their CSIRT or competent authority without undue delay of any significant incident. This includes notifying the CSIRT or competent authority within 24 hours of becoming aware of the incident, indicating whether the incident is suspected of being caused by unlawful or malicious acts. They must also notify the CSIRT or competent authority within 72 hours of becoming aware of the incident, updating the early warning and providing an initial assessment of severity and impact. Finally, they must provide a final report within one month after the incident notification, including a detailed description, root cause analysis, mitigation measures, and cross-border impact where applicable.\n\n- **AI Act**: The entity must notify the market surveillance authority immediately, not later than 15 days after becoming aware of the incident. Additionally, they must notify affected individuals if the incident is likely to cause high risk to their rights and freedoms, such as privacy, integrity of personal data, or freedom of expression.\n\nIn summary, the AI Act requires a notification to the market surveillance authority within 15 days, and potentially to affected individuals if the incident is high",
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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 AI Act's conformity assessment interacts with existing product safety legislation for AI-embedded products in a way that aims to ensure both the safety and the compliance of AI systems. Here's how it works:\n\n1. **Compliance with AI Act**: The AI Act requires that AI systems conform to the requirements of the AI Act, particularly for high-risk AI systems. This means that AI systems must meet specific criteria related to their use, functionality, and potential risks.\n\n2. **Conformity Assessment**: The AI Act mandates a conformity assessment for high-risk AI systems. This assessment is conducted by a notified body, which is an independent body recognized by the European Commission. The notified body verifies that the AI system meets the requirements of the AI Act.\n\n3. **Integration with Product Safety Legislation**: The conformity assessment under the AI Act is designed to complement rather than replace existing product safety legislation. This means that AI systems must not only comply with the AI Act but also with existing product safety legislation. For example, if an AI system is also a product subject to product safety legislation, it must meet both sets of requirements.\n\n4. **CE Marking**: High-risk AI systems that pass the conformity assessment are required to be affixed with the CE marking. The CE marking indicates that the",
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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": "### Regulatory Obligations, Timelines, Authorities, and Potential Penalties for a Bank Deploying a Credit Scoring AI System Experiencing a Data Breach\n\n#### Regulatory Obligations\n\n1. **AI Act**\n   - **Annex III Category 5b**: Deployer of credit scoring AI must comply with the AI Act's high-risk requirements (Art. 9-15), including conformity assessment and CE marking.\n   - **GDPR**: Art. 22 (right not to be subject to automated decisions), Art. 35 (DPIA mandatory), Art. 13-14 (transparency).\n   - **DORA**: ICT risk management (Art. 5-7), third-party risk (Art. 28-30), incident reporting (Art. 17-19), resilience testing (Art. 24-27).\n   - **EBA Guidelines**: on creditworthiness assessment, on use of ML in internal models.\n   - **National Consumer Protection Law**: Compliance with national laws specific to credit scoring and data breach notifications.\n\n2. **NIS2**\n   - **Essential Entity**: The bank is an essential entity under NIS2, so it",
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        "convergence_rounds": 1,
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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": "For the AI system deployed by the energy utility for grid management, which is considered high-risk AI under Annex III Category 2 (critical infrastructure), the following obligations and responsibilities would be distributed among the AI model provider, the AI system provider, and the energy utility (deployer) based on the AI Act, DORA, and NIS2:\n\n### AI Act Obligations:\n1. **AI Model Provider**: \n   - The AI model provider would be responsible for ensuring that the model they supply complies with the requirements of the AI Act, including the conformity assessment and the EU declaration of conformity. They would also be responsible for the model card and copyright compliance, as well as the training data summary.\n   \n2. **AI System Provider (Fintech)**:\n   - The AI system provider would be responsible for integrating the model into their system, ensuring compliance with the AI Act requirements (Art. 9-15), and conducting the conformity assessment.\n   \n3. **Deployer (Energy Utility)**:\n   - The energy utility would be responsible for ensuring human oversight, monitoring the performance of the system, and reporting any incidents. They would also be responsible for ensuring that the system is used in accordance with the intended purpose and for any substantial modifications made by",
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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 for a Hospital Deploying an AI Diagnostic System\n\n#### 1. **Identify High-Risk AI Systems**\n   - **Annex III Category 1 (Biometric Identification)**: The AI diagnostic system uses biometric data for patient identification and tracking.\n   - **Annex III Category 2 (Critical Infrastructure)**: The system is used in critical hospital operations, such as emergency response and patient monitoring.\n   - **Annex III Category 4 (Employment)**: The system is used in employment decisions, such as hiring and performance evaluations.\n\n#### 2. **Conformity Assessment and CE Marking**\n   - **AI Act Conformity Assessment**: Complete conformity assessment as per AI Act Article 43. Obtain CE marking as per Article 48.\n   - **EU Declaration of Conformity**: Draft and sign the EU declaration of conformity as per Article 47.\n   - **Registration**: Register the AI system in the EU database as per Article 49.\n\n#### 3. **Fundamental Rights Impact Assessment (FRIA)**\n   - **FRIA for Annex III Category 4 (Employment)**: Conduct a FRIA to assess the impact on fundamental rights, particularly rights of the worker (",
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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": "To determine 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, we need to consider the maximum penalties for each of these violations and sum them up.\n\n1. **Prohibited AI Practices (AI Act Art. 99)**: The maximum fine is 7% of the company's global annual turnover.\n2. **GDPR Violations (GDPR Art. 83)**: The maximum fine is 4% of the company's global annual turnover.\n3. **NIS2 Violations (NIS2 Art. 34)**: The maximum fine is 2% of the company's global annual turnover.\n4. **NIS2 Violations (NIS2 Art. 34)**: The maximum fine is 2% of the company's global annual turnover.\n\nThe total maximum fine exposure is the sum of these maximum fines:\n\\[ 7\\% + 4\\% + 2\\% + 2\\% = 15\\% \\]\n\nTherefore, the total maximum fine exposure is 15% of the company's global annual turnover.",
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        "total_tokens": 13146,
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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 while considering the AI Act's territorial scope, GDPR's cross-border processing rules, and varying NIS2 transposition requires a comprehensive and coordinated approach. Here's a structured way to handle this:\n\n### 1. **Identify High-Risk AI Systems**\nFirst, determine if your AI recruitment system falls under the high-risk categories of Annex III of the AI Act (e.g., employment, workers management, access to self-employment). If it does, you need to comply with the AI Act's high-risk requirements.\n\n### 2. **CE Marking and Conformity Assessment**\nFor high-risk AI systems, affix the CE marking and complete the conformity assessment as required by the AI Act. This involves:\n- **Conformity Assessment**: Complete the conformity assessment as per the AI Act's requirements.\n- **EU Declaration of Conformity**: Draft the EU declaration of conformity, which states the provider's responsibility for compliance.\n- **Registration**: Register the system in the EU database for high-risk AI systems.\n\n### 3. **GDPR Compliance**\nGiven the AI Act's territorial scope and the GDPR's cross-border processing rules, ensure that your AI recruitment system complies with GDPR:\n- **Art.",
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        "cost_usd": 0.0013167,
        "total_tokens": 13167,
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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": "To use real-time biometric identification in a public space for preventing an imminent terrorist threat, the law enforcement agency must navigate through a complex web of legal frameworks and ensure compliance with all applicable conditions and oversight requirements. Here is a comprehensive pathway and conditions for such a use case:\n\n### Legal Pathway and Conditions\n\n1. **Annex III High-Risk Use Cases**\n   - **Category 1 (Biometric Identification and Categorisation)**: The real-time biometric identification system would fall under this category, as it involves the use of biometric data for identification purposes.\n   - **AI Act Compliance**: The AI Act requires high-risk AI systems to be compliant with Art. 9-15, including conformity assessment, CE marking, and registration in the EU database. The system must also be transparent and explainable, and it must not cause significant harm to individuals.\n   - **GDPR Compliance**: The system must comply with GDPR Art. 22 (right not to be subject to automated decisions), Art. 35 (DPIA mandatory), and Art. 13-14 (transparency). It must also be subject to national biometric laws, which may require additional oversight.\n   - **DORA Compliance**: The system must",
        "exact_match": 0.0,
        "f1": 0.33757961783439494,
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        "cost_usd": 0.0013176000000000002,
        "total_tokens": 13176,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
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        "constraint_propagations": 0,
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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": "### Enforcement Cascade Across AI Act, GDPR, and Potential Liability\n\n#### Initial Findings and Initial Actions\n1. **Data Protection Authority (DPA) Finds Bias in Training Data**:\n   - The DPA identifies that the training data for the AI risk assessment system is biased against certain ethnic groups, violating the principle of non-discrimination under the GDPR (Article 21).\n   - The DPA issues a formal notice to the insurance company, requiring them to cease using the biased AI system and to provide a detailed explanation of how they will address the bias.\n\n2. **Insurance Company Response**:\n   - The insurance company acknowledges the bias and commits to addressing it by retraining the AI model with a diverse and representative dataset.\n   - They also commit to conducting a Fundamental Rights Impact Assessment (FRIA) to evaluate the impact of the AI system on fundamental rights, as required by the AI Act (Article 26).\n\n3. **AI Act Compliance**:\n   - The insurance company must ensure that the AI system complies with the AI Act requirements, including conducting a FRIA, ensuring human oversight, and reporting incidents (Article 26).\n   - They must also ensure that the AI system is compliant with the AI Act's high-risk requirements (Article",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 4003.912900108844,
        "cost_usd": 0.0013193000000000002,
        "total_tokens": 13193,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
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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": "For a fintech company running AI-based fraud detection systems, the AI Act's quality management system (QMS) requirement interacts with DORA's ICT governance and NIS2's cybersecurity policies in several ways:\n\n1. **AI Act Quality Management System (QMS):** The AI Act mandates that high-risk AI systems, including those used for fraud detection, must have a QMS in place. This QMS should include processes for ensuring the AI system's compliance with the AI Act's requirements, such as conformity assessment, EU declaration of conformity, and CE marking. The QMS should also ensure that the AI system is free from bias and that it is transparent and explainable. The QMS should be documented and maintained to ensure ongoing compliance with the AI Act.\n\n2. **DORA's ICT Governance:** DORA's requirements for ICT governance include third-party risk management, contractual safeguards, and incident reporting. Since the AI system is used for fraud detection, which involves high-risk use cases (as per Annex III of the AI Act), the QMS should align with DORA's requirements. For instance, the QMS should include processes for vetting AI model providers, verifying training data provenance, and assessing open-source model security. Additionally, the QMS should ensure",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 3988.73919993639,
        "cost_usd": 0.0013178,
        "total_tokens": 13178,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
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        "constraint_propagations": 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": "For a single AI incident in a critical infrastructure bank, the incident reporting timelines, authorities, and templates vary significantly across AI Act, GDPR, DORA, and NIS2. Here's a comparison:\n\n### Incident Reporting Timelines\n- **AI Act**: \"immediately, not later than 15 days\" to the market surveillance authority.\n- **GDPR**: \"not later than 72 hours\" to the DPA and affected individuals if high risk.\n- **DORA**: \"4 hours initial\" to the CSIRT or competent authority.\n- **NIS2**: \"24 hours early warning\" within 24 hours of becoming aware, then \"72 hours incident notification\" and \"one month final report\".\n\n### Authorities Involved\n- **AI Act**: Market surveillance authority.\n- **GDPR**: DPA and affected individuals if high risk.\n- **DORA**: CSIRT or competent authority.\n- **NIS2**: CSIRT or competent authority.\n\n### Templates and Information Required\n- **AI Act**: Not specified in detail, but typically requires reporting the nature of the incident, suspected causes, and preliminary impact.\n- **GDPR**: Requires detailed information on the nature of the incident, the affected individuals, and the measures",
        "exact_match": 0.0,
        "f1": 0.5454545454545454,
        "latency_ms": 4013.8477999716997,
        "cost_usd": 0.0013157000000000002,
        "total_tokens": 13157,
        "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": "When an AI regulatory sandbox participant wants to test a novel emotion recognition system in a workplace setting, several legal boundaries, exceptions, and framework interactions need to be considered. Here are the key points to consider:\n\n### Legal Boundaries\n1. **Privacy and Data Protection**: The AI system must comply with data protection laws, such as the General Data Protection Regulation (GDPR). The system should not collect, process, or store biometric data without explicit consent, and it should be transparent about how data is used and protected.\n2. **Employment Law**: The system should not be used in a way that violates workers' rights, such as automating decisions that could lead to unfair or discriminatory outcomes. The system should be used in a way that aligns with the principles of the European Union's Charter of Fundamental Rights, including the right to a fair labor contract.\n3. **Workplace Safety**: The system should not pose any risk to the physical safety of workers. Any safety concerns should be thoroughly assessed and mitigated.\n4. **Consumer Protection**: If the system is used to provide services to consumers, it should comply with consumer protection laws, including the General Product Safety Directive (GPSD) and the Consumer Rights Directive.\n\n### Exceptions and Framework Interactions\n1.",
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        "convergence_rounds": 1,
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  "cognigraph-pcst-v2": {
    "summary": {
      "method": "cognigraph-pcst-v2",
      "dataset": "MultiGov-30-v2",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.32331064342315197,
      "avg_latency_ms": 33004.072093342744,
      "avg_cost_usd": 1.6266666666666668e-05,
      "avg_tokens": 162.66666666666666,
      "avg_rounds": 2.0,
      "avg_nodes": 8.0,
      "total_cost_usd": 0.000488,
      "total_latency_s": 990.1221628002822,
      "total_shacl_pass": 3546,
      "total_shacl_fail": 3894,
      "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": "Under the EU AI Act, the prohibition of subliminal techniques and social scoring by public authorities are explicitly prohibited. Penalties for these violations include fines up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher, with enforcement starting on 2 February 2025. These prohibitions apply simultaneously, and compliance with the EU AI Act is mandatory for all AI practices, as there are no simultaneous exemptions for these specific practices.",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 44242.24090017378,
        "cost_usd": 1.63e-05,
        "total_tokens": 163,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 16,
        "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": "The complete compliance chain for a provider to place a high-risk AI system on the EU market involves inventorying all AI systems, classifying them against Article 5 of the AI Act, identifying any emotion recognition in workplace or education, reviewing biometric processing activities, and checking for dark patterns or manipulative AI. Systems found to violate Article 5 must be decommissioned immediately, with no transition period. Providers must also be prepared for potential penalties up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher, starting from 2 February 2025. If multiple frameworks apply simultaneously, the AI Act's requirements take precedence.",
        "exact_match": 0.0,
        "f1": 0.20618556701030927,
        "latency_ms": 24048.38849976659,
        "cost_usd": 1.4900000000000001e-05,
        "total_tokens": 149,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
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        "shacl_fail": 19,
        "constraint_propagations": 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": "Deployers of high-risk AI systems are not specifically addressed in the EU AI Act regarding human oversight obligations. However, Article 5 of the AI Act prohibits certain AI practices, such as social scoring and real-time remote biometric identification, which could involve human oversight issues. While the EU AI Act does not provide detailed guidelines for human oversight, deployers should ensure their systems do not violate these prohibitions. Given the lack of specific provisions, it is important for deployers to adhere to the general prohibitions outlined in Article 5 and to consider broader governance frameworks that may apply simultaneously, such as data protection regulations and ethical guidelines, to ensure compliance and mitigate risks.",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 22177.993500139564,
        "cost_usd": 1.7e-05,
        "total_tokens": 170,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 26,
        "shacl_fail": 22,
        "constraint_propagations": 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": "The EU AI Act imposes no specific requirements on general-purpose AI models with systemic risk within its current framework. This is consistent with Article 5, which focuses on prohibited AI practices. Therefore, there are no explicit requirements for general-purpose AI models with systemic risk under the current EU AI Act. Simultaneously, the Act does not address these models, leaving them subject to the broader governance constraints and potential penalties for non-compliance, as outlined in other relevant articles.",
        "exact_match": 0.0,
        "f1": 0.16666666666666666,
        "latency_ms": 23658.472000155598,
        "cost_usd": 9.3e-06,
        "total_tokens": 93,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 35,
        "shacl_fail": 29,
        "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 General Data Protection Regulation (GDPR) outlines specific principles for processing personal data, including principles such as purpose limitation, data minimization, and transparency (Article 5). These principles apply to the processing of personal data, including in the context of AI training. However, the EU AI Act and GDPR are separate frameworks that operate concurrently. The GDPR does not address the specific governance constraints of AI training, such as prohibiting discrimination, ensuring non-discrimination, and preventing harm (Article 22 of the AI Act). Compliance with GDPR does not automatically ensure compliance with the AI Act, and vice versa. Therefore, AI training must adhere to both sets of guidelines to ensure proper governance and protection of personal data.",
        "exact_match": 0.0,
        "f1": 0.3798882681564246,
        "latency_ms": 24855.18170008436,
        "cost_usd": 1.0800000000000002e-05,
        "total_tokens": 108,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 44,
        "shacl_fail": 36,
        "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": "The EU AI Act does not explicitly address transparency obligations for AI systems generating deepfakes or synthetic content. While Article 5 of the Act focuses on prohibited AI practices, it does not cover transparency requirements for these specific applications. Therefore, there are no specific transparency obligations cited in the EU AI Act for such AI systems.",
        "exact_match": 0.0,
        "f1": 0.4,
        "latency_ms": 34674.739399924874,
        "cost_usd": 2.8300000000000003e-05,
        "total_tokens": 283,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 49,
        "shacl_fail": 47,
        "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 query about DORA's ICT risk management requirements for financial entities is outside my domain, as my expertise focuses on the EU AI Act, particularly Article 5. DORA, which stands for Development, Operations, and DevOps Enablement, is not a regulatory framework related to financial risk management. Therefore, I cannot provide specific DORA requirements for financial entities. However, it is important to note that financial entities are subject to various governance constraints, including those outlined in the EU AI Act, which can influence their risk management practices.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 22145.3761998564,
        "cost_usd": 2.11e-05,
        "total_tokens": 211,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 60,
        "shacl_fail": 52,
        "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 query about incident notification timelines for NIS2 essential entities is outside the scope of the EU AI Act, specifically Article 5 and related governance requirements. My expertise lies in analyzing and understanding the EU AI Act and its implications for prohibited AI practices. Therefore, I cannot provide information about NIS2 incident notification timelines. NIS2 (Network and Information Systems) regulations are governed by different articles and frameworks, such as Article 17 of the NIS Directive, which includes requirements for incident notification. However, these are distinct from the EU AI Act and do not overlap in terms of governance.",
        "exact_match": 0.0,
        "f1": 0.16560509554140126,
        "latency_ms": 23613.578600343317,
        "cost_usd": 1.65e-05,
        "total_tokens": 165,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 71,
        "shacl_fail": 57,
        "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": "Annex III of the AI Act does not specifically define high-risk AI use cases for employment and workers management. However, within my domain, high-risk AI use cases for employment and workers management include emotion recognition in workplace and educational institutions (Article 5(6)). Annex III of the AI Act also addresses social scoring by or on behalf of public authorities (Article 5(1)(c)), which is a high-risk use case. These use cases are subject to penalties and timelines as outlined in Article 5, and they operate simultaneously, requiring robust governance and oversight to ensure compliance.",
        "exact_match": 0.0,
        "f1": 0.2435897435897436,
        "latency_ms": 27856.226000003517,
        "cost_usd": 1.6700000000000003e-05,
        "total_tokens": 167,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 82,
        "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": "The EU AI Act does not explicitly define the role or scope of AI regulatory sandboxes. However, it primarily focuses on prohibiting certain AI practices under Article 5. AI regulatory sandboxes are not mentioned in the Act. If they were to be established, they would likely operate alongside other governance frameworks, such as Article 5, to ensure a comprehensive regulatory approach. Compliance with the EU AI Act would require adherence to its prohibitions and other relevant articles, rather than relying on AI regulatory sandboxes.",
        "exact_match": 0.0,
        "f1": 0.23841059602649006,
        "latency_ms": 17548.359900247306,
        "cost_usd": 8.700000000000001e-06,
        "total_tokens": 87,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 96,
        "shacl_fail": 64,
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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 jointly regulate automated decision-making that affects natural persons by prohibiting prohibited AI practices such as social scoring (Article 5(1)(c)) and exploiting vulnerabilities (Article 5(1)(b)) under the AI Act. Simultaneously, the GDPR ensures that individuals' rights are not solely automated without human review (Article 22). Both frameworks operate simultaneously, with the GDPR providing a broader scope of individual rights protection and the AI Act focusing on the prohibition of certain AI practices that could harm individuals.",
        "exact_match": 0.0,
        "f1": 0.3151515151515151,
        "latency_ms": 29434.16569987312,
        "cost_usd": 1.16e-05,
        "total_tokens": 116,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 105,
        "shacl_fail": 71,
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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": "When a high-risk AI system violates both the EU AI Act and GDPR, the combined penalty exposure can reach up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher (Article 99(3) of the AI Act), reflecting the severity of deploying banned practices. Enforcement begins 2 February 2025 (six months after the entry into force of the AI Act). Both the EU AI Act and GDPR apply simultaneously, and their penalties interact to potentially impose the highest tier of penalties on the violating entity.",
        "exact_match": 0.0,
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        "latency_ms": 39673.2029998675,
        "cost_usd": 1.46e-05,
        "total_tokens": 146,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 106,
        "shacl_fail": 86,
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      },
      {
        "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": "To comply with both the AI Act's fundamental rights impact assessment and GDPR's data protection impact assessment for a high-risk AI system, a deployer must conduct a comprehensive impact assessment that covers both frameworks. This includes identifying potential risks to fundamental rights and data protection, and implementing measures to mitigate these risks. Simultaneously, the AI system must be audited against Article 5 of the AI Act to ensure it does not engage in prohibited practices, and a GDPR data protection impact assessment must be conducted as per Article 35 of the GDPR, ensuring compliance with data minimization, accuracy, and storage limitation principles. Both assessments must be completed within the specified timelines of 2 February 2025, ensuring that the AI system does not violate any prohibited practices and adheres to data protection requirements.",
        "exact_match": 0.0,
        "f1": 0.2976744186046512,
        "latency_ms": 35249.63770015165,
        "cost_usd": 1.9600000000000002e-05,
        "total_tokens": 196,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 112,
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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": "When a financial entity reports an AI-related incident under both the EU AI Act and the DORA framework, the dual obligations apply specifically to Article 5 of the EU AI Act, which prohibits the use of certain AI applications that constitute a significant risk to individuals or society. However, since the DORA framework is not within the scope of the EU AI Act, the entity must adhere to the governance constraints of the EU AI Act alone. There are no specific dual obligations or interactions between the EU AI Act and DORA in this context.",
        "exact_match": 0.0,
        "f1": 0.2681564245810055,
        "latency_ms": 38408.32650009543,
        "cost_usd": 1.32e-05,
        "total_tokens": 132,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 117,
        "shacl_fail": 107,
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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": "The AI Act's cybersecurity requirements for high-risk AI systems do not directly interact with the NIS2 security measures for essential entities. Article 5 of the AI Act focuses on the governance and deployment of AI systems, whereas Article 2 of NIS2 addresses cybersecurity for essential entities. These frameworks apply simultaneously, and their interaction is not specified in the current governance constraints. Therefore, compliance with the AI Act's cybersecurity requirements for high-risk AI does not automatically imply compliance with NIS2 security measures for essential entities, and vice versa.",
        "exact_match": 0.0,
        "f1": 0.3146067415730337,
        "latency_ms": 24932.459500152618,
        "cost_usd": 7.9e-06,
        "total_tokens": 79,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 125,
        "shacl_fail": 115,
        "constraint_propagations": 0,
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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": "The GDPR's data protection by design principle aligns with the AI Act's data governance requirements, as both emphasize incorporating data protection measures from the design phase. Article 5 of the GDPR prohibits certain AI practices that could exploit vulnerabilities or cause significant harm, which is consistent with the AI Act's Article 5. This alignment ensures that AI systems are developed and used in a manner that respects individuals' rights and freedoms, thereby reinforcing the GDPR's data protection objectives. Both frameworks apply simultaneously, ensuring a comprehensive approach to data protection and AI development.",
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        "latency_ms": 31908.544200006872,
        "cost_usd": 1.4000000000000001e-05,
        "total_tokens": 140,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 131,
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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": "When a financial institution uses a third-party high-risk AI system, the implications under the AI Act are significant. Article 5(1)(c) of the AI Act prohibits social scoring by public authorities or on their behalf, and any use of such a system by the financial institution could lead to penalties for non-compliance. Given the dual governance constraints of the AI Act and DORA, the financial institution must ensure that the AI system does not engage in prohibited practices, such as social scoring, to avoid potential penalties. The specific supply chain implications for a high-risk AI system would require further analysis within the scope of both the AI Act and DORA guidelines.",
        "exact_match": 0.0,
        "f1": 0.37037037037037035,
        "latency_ms": 40967.229299712926,
        "cost_usd": 2.12e-05,
        "total_tokens": 212,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 134,
        "shacl_fail": 138,
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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 query is outside my domain, but assuming the context is related to the EU AI Act, Article 5 of the AI Act addresses the prohibition of certain prohibited purposes for AI systems. The DORA ICT risk management framework for credit scoring AI is not directly related to the EU AI Act. However, if both frameworks apply simultaneously, they would need to be managed concurrently, ensuring that the AI system does not engage in prohibited purposes as per Article 5 while also adhering to the risk management requirements of DORA ICT. Without specific evidence of compliance, I cannot claim that the AI system meets the requirements of the EU AI Act or DORA ICT risk management for credit scoring AI.",
        "exact_match": 0.0,
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        "latency_ms": 29911.44290007651,
        "cost_usd": 8.8e-06,
        "total_tokens": 88,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
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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": "This query is outside my domain, as my expertise is in the EU AI Act, particularly Article 5. I do not have information regarding dual incident notification requirements for cybersecurity breaches affecting essential infrastructure under the NIS2 directive. Therefore, I cannot provide specific requirements, penalties, or timelines for these dual incident notification requirements. It is important to consult the relevant authorities or documents for the most accurate and up-to-date information on how the AI Act and NIS2 interact in this specific scenario.",
        "exact_match": 0.0,
        "f1": 0.21951219512195122,
        "latency_ms": 31754.703199956566,
        "cost_usd": 1.11e-05,
        "total_tokens": 111,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 144,
        "shacl_fail": 160,
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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 AI Act's conformity assessment does not directly interact with existing product safety legislation for AI-embedded products within my domain constraints. Article 5 of the AI Act focuses on prohibiting high-risk AI applications, while product safety legislation typically addresses safety and quality standards for AI-embedded products. These frameworks do not apply simultaneously to the same products, and thus, there is no direct interaction between the AI Act's conformity assessment and existing product safety legislation for AI-embedded products.",
        "exact_match": 0.0,
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        "latency_ms": 29893.73890031129,
        "cost_usd": 9.3e-06,
        "total_tokens": 93,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 148,
        "shacl_fail": 172,
        "constraint_propagations": 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 bank must comply with Article 5 of the AI Act and GDPR Article 22 to ensure the credit scoring AI system does not violate the right to not be subject to solely automated decision-making. Violations under Article 5 of the AI Act also violate GDPR Article 22, leading to cumulative liability exposure. Penalties include up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher (Art. 99(3) of the AI Act), and fines under GDPR Art. 83 for non-compliance. The AI Office and national market surveillance authorities are responsible for enforcement, with enforcement starting on 2 February 2025, and the bank must ensure the system is audited for compliance with Article 5 of the AI Act immediately.",
        "exact_match": 0.0,
        "f1": 0.30830039525691705,
        "latency_ms": 65392.41879992187,
        "cost_usd": 2.8800000000000002e-05,
        "total_tokens": 288,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 153,
        "shacl_fail": 183,
        "constraint_propagations": 0,
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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": "Under the EU AI Act, the energy utility would be primarily responsible for ensuring compliance with Article 5, which prohibits certain AI practices. The third-party provider would be responsible for ensuring that the AI system does not violate Article 5. Both entities would need to conduct an obligation analysis and identify any prohibited practices, as outlined in Article 5. Enforcement would be handled by national market surveillance authorities, as per Article 99(3). Simultaneously, under the Digital Operational Readiness and Digital Operational Excellence (DORA) framework and the Network and Information Systems (NIS) Directive, the energy utility would be responsible for ensuring that the AI system operates securely and reliably, adhering to the requirements of the NIS Directive, and the third-party provider would be responsible for ensuring that the AI system does not compromise the security of the energy utility's critical infrastructure. Enforcement for these frameworks would be handled by relevant national authorities. Both sets of obligations are interrelated and must be considered in the context of the governance constraints and the specific requirements of each framework.",
        "exact_match": 0.0,
        "f1": 0.2206405693950178,
        "latency_ms": 35473.70330011472,
        "cost_usd": 1.94e-05,
        "total_tokens": 194,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 161,
        "shacl_fail": 191,
        "constraint_propagations": 0,
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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": "Designing a comprehensive compliance program for a hospital deploying an AI diagnostic system requires balancing innovation with data protection, ensuring transparency in AI use, and maintaining public trust. Simultaneously, the hospital must comply with the AI Act, GDPR, NIS2, and fundamental rights. The AI Act prohibits the use of subliminal techniques or exploiting vulnerabilities, aligning with GDPR's data processing principles and the right to be informed. NIS2 mandates the hospital to ensure the AI system's cybersecurity and resilience. Fundamental rights, particularly privacy and data protection, must be upheld. To achieve compliance, the hospital should inventory all AI systems, classify them against the AI Act, and review biometric processing activities and emotion recognition in healthcare settings. Continuous monitoring and a robust audit process are essential to ensure alignment with these frameworks, which apply simultaneously and interact, requiring a coordinated approach to address the key tensions between them.",
        "exact_match": 0.0,
        "f1": 0.2569444444444444,
        "latency_ms": 55614.48419978842,
        "cost_usd": 2.36e-05,
        "total_tokens": 236,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 161,
        "shacl_fail": 207,
        "constraint_propagations": 0,
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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": "The 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 is up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher (Article 99(3) of the EU AI Act). Given that the company also violates GDPR data protection and NIS2 cybersecurity, these fines apply simultaneously. The EU AI Act, GDPR, and NIS2 are interconnected, and non-compliance with any of these regulations can result in significant penalties. Therefore, the company's total maximum fine exposure is the sum of the fines under these interconnected regulations.",
        "exact_match": 0.0,
        "f1": 0.35238095238095235,
        "latency_ms": 39103.5688999109,
        "cost_usd": 2e-05,
        "total_tokens": 200,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 166,
        "shacl_fail": 218,
        "constraint_propagations": 0,
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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": "To deploy an AI recruitment system across the EU, a multinational should ensure all AI systems comply with the AI Act's Article 5, prohibiting social scoring and manipulative techniques, and decommission any prohibited practices. Given the territorial scope of the AI Act and GDPR's cross-border processing rules, they must also comply with GDPR Articles 22 (right not to be subject to solely automated decisions) and 9 (special categories of data). Simultaneously, they need to consider the varying NIS2 transposition requirements for sector-specific regulations. Compliance with these frameworks is crucial for a seamless deployment, ensuring that the system does not violate any of the specified rights or prohibitions.",
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        "f1": 0.375,
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        "cost_usd": 1.98e-05,
        "total_tokens": 198,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
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        "shacl_fail": 232,
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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": "The law enforcement agency can use real-time biometric identification in a public space to prevent an imminent terrorist threat, as this practice is not prohibited under the EU AI Act Article 5(1)(d). However, the agency must ensure compliance with the General Data Protection Regulation (GDPR) and other relevant data protection laws. Given the imminent nature of the threat, the agency must begin auditing their AI systems and reviewing biometric processing activities by February 2, 2025, as mandated by the EU AI Act. Simultaneously, the agency must adhere to the General Data Protection Regulation (GDPR) and other applicable data protection laws, ensuring that the use of biometric data complies with Article 5(1)(d) of the EU AI Act and Article 22 of the GDPR. Failure to comply with these requirements could result in penalties and legal action.",
        "exact_match": 0.0,
        "f1": 0.27306273062730624,
        "latency_ms": 32747.300299815834,
        "cost_usd": 1.73e-05,
        "total_tokens": 173,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 181,
        "shacl_fail": 235,
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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 insurance company's use of biased training data for AI risk assessment violates Article 5(1)(b) of the AI Act, which prohibits exploiting vulnerabilities of specific groups, and could also violate GDPR Articles 22 and 9. Given the prohibition under the AI Act, the company would be in violation of multiple provisions, including GDPR. The enforcement cascade would involve the AI Office coordinating with national market surveillance authorities to enforce the prohibition. Penalties for such violations can reach up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher, as per Article 99(3) of the AI Act. Since the AI Act and GDPR apply simultaneously, the company would face penalties under both frameworks, with the higher of the two thresholds determining the final penalty.",
        "exact_match": 0.0,
        "f1": 0.27376425855513303,
        "latency_ms": 46060.87819999084,
        "cost_usd": 2.9700000000000004e-05,
        "total_tokens": 297,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 190,
        "shacl_fail": 242,
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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": "According to Article 5 of the AI Act, the quality management system requirement interacts with DORA's ICT governance and NIS2's cybersecurity policies by ensuring that AI-based fraud detection systems are robust and secure, aligning with GDPR's data protection principles. This integration helps in maintaining compliance with multiple regulations, though simultaneous application of these frameworks requires careful alignment to avoid overlapping or conflicting requirements.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 27217.67839975655,
        "cost_usd": 9.800000000000001e-06,
        "total_tokens": 98,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 197,
        "shacl_fail": 251,
        "constraint_propagations": 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": "The AI Act, GDPR, DORA, and NIS2 do not provide specific incident reporting timelines, authorities, or templates for AI incidents in critical infrastructure banks. The AI Act, for instance, does not mandate incident reporting timelines or templates, focusing instead on the prohibition of prohibited purposes (Article 5). The GDPR, while providing for data protection incident reporting, does not address AI incidents specifically. DORA, which is related to cybersecurity, does not provide incident reporting requirements for AI incidents. The NIS2, which focuses on network and information systems, also does not specify incident reporting for AI incidents. Given the lack of specific provisions for AI incidents in these frameworks, it is not possible to compare and contrast their incident reporting timelines, authorities, and templates for a single AI incident in a critical infrastructure bank. The governance constraints of these frameworks do not allow for the application of these provisions to AI incidents in critical infrastructure banks.",
        "exact_match": 0.0,
        "f1": 0.22602739726027396,
        "latency_ms": 25065.190500114113,
        "cost_usd": 1.4700000000000002e-05,
        "total_tokens": 147,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 208,
        "shacl_fail": 256,
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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": "An AI regulatory sandbox participant testing a novel emotion recognition system in a workplace setting would likely violate the EU AI Act, specifically Article 5(6), which prohibits emotion recognition in workplace and educational institutions. Given this prohibition, there are no exceptions within Article 5 for such testing. Organizations must conduct an audit of existing AI systems and review biometric processing activities to ensure compliance. Multiple frameworks apply simultaneously, including the EU AI Act, but compliance requires a thorough review and potential modification of the emotion recognition system to avoid violating these governance constraints.",
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        "latency_ms": 23128.15529992804,
        "cost_usd": 1.4000000000000001e-05,
        "total_tokens": 140,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 223,
        "shacl_fail": 257,
        "constraint_propagations": 0,
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    ]
  }
}