{
  "single-agent": {
    "summary": {
      "method": "single-agent",
      "dataset": "MultiGov-30",
      "n_questions": 20,
      "avg_em": 0.0,
      "avg_f1": 0.34883965177128584,
      "avg_latency_ms": 3119.200855004601,
      "avg_cost_usd": 0.0013169000000000002,
      "avg_tokens": 13169.0,
      "avg_rounds": 1.0,
      "avg_nodes": 1.0,
      "total_cost_usd": 0.026338000000000004,
      "total_latency_s": 62.384017100092024
    },
    "questions": [
      {
        "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 their complementary requirements and scopes. The GDPR provides specific protections for automated decision-making, particularly for automated decision-making that produces legal effects concerning a natural person or significantly affects them. The GDPR requires that such automated decision-making be transparent, and natural persons have the right to obtain human intervention, express their own view, and obtain an explanation of the decision. Additionally, the GDPR mandates a Data Protection Impact Assessment (DPIA) for high-risk automated decision-making processes.\n\nOn the other hand, the AI Act focuses on the quality and safety of AI systems, particularly high-risk AI systems, and sets out specific requirements for the use of AI in various sectors. The AI Act requires that high-risk AI systems are designed and used in a way that minimizes risks to individuals and ensures that they are transparent and explainable. For high-risk AI systems, the AI Act mandates a conformity assessment and a EU Declaration of Conformity.\n\nBoth regulations aim to ensure that automated decision-making processes are fair, transparent, and respect the rights of individuals. The GDPR provides the rights and protections for individuals, while the AI Act ensures that AI systems are safe and reliable. Together, they provide a comprehensive framework for regulating automated decision",
        "exact_match": 0.0,
        "f1": 0.3028169014084507,
        "latency_ms": 3139.48730006814,
        "cost_usd": 0.0013193000000000002,
        "total_tokens": 13193,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 combined penalty exposure when a high-risk AI system violates both the EU AI Act and GDPR can be calculated by multiplying the penalties from each regulation. The AI Act imposes a penalty of up to EUR 30 million or 6% of the total worldwide annual revenue of the AI system provider, whichever is higher. The GDPR imposes a penalty of up to EUR 20 million or 4% of the total worldwide annual revenue of the organization, whichever is higher.\n\nFor a high-risk AI system, the AI Act penalty is typically higher, as it applies to the AI system provider, who is often the entity that can bear the higher financial burden. Therefore, the combined penalty exposure would be:\n\n- AI Act penalty: Up to EUR 30 million or 6% of the AI system provider's revenue.\n- GDPR penalty: Up to EUR 20 million or 4% of the organization's revenue.\n\nIn the worst-case scenario, assuming the AI system provider's revenue is the same as the organization's revenue (which is not always the case), the combined penalty would be:\n\n- Combined penalty: Up to EUR 30 million (AI Act) + EUR 20 million (GDPR) = Up to EUR 50 million.\n\nHowever,",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 3138.0714997649193,
        "cost_usd": 0.0013180000000000002,
        "total_tokens": 13180,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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) by ensuring that each assessment is conducted and addressed in a way that aligns with the requirements of both regulations. Here's how they can comply:\n\n1. **Conduct FRIA**: The deployer must conduct a FRIA in accordance with the AI Act's requirements. This involves considering the fundamental rights impacts of the AI system, such as discrimination, access to services, labor rights, and environmental rights. The FRIA should be conducted before the AI system is deployed to ensure that all necessary considerations are made.\n\n2. **Conduct DPIA**: The deployer must also conduct a DPIA in accordance with GDPR's requirements. This involves assessing the risks to individuals' personal data and ensuring that appropriate data protection measures are in place. The DPIA should be conducted before the AI system is deployed to ensure that all necessary data protection measures are in place.\n\n3. **Ensure Compliance**: The deployer must ensure that the FRIA and DPIA are conducted in a way that satisfies both sets of requirements. This might involve ensuring that the FRIA covers all the necessary aspects of the AI system's",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 3154.929999727756,
        "cost_usd": 0.0013189,
        "total_tokens": 13189,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 are specific to the requirements of each regulation. Specifically, they must ensure that the AI system they are using complies with the requirements of the AI Act, including conducting conformity assessments, drawing up EU declarations of conformity, and registering the system in the EU database for high-risk AI systems. This ensures that the AI system meets the high-risk requirements of the AI Act.\n\nAdditionally, the entity must comply with the requirements of DORA, which includes third-party risk management, contractual safeguards, and exit strategies. They must also ensure that they are conducting incident reporting and resilience testing as required by DORA.\n\nIn summary, the financial entity must fulfill the obligations of both the AI Act and DORA to ensure that the AI system is compliant and that the incident reporting and related obligations are met.",
        "exact_match": 0.0,
        "f1": 0.24786324786324787,
        "latency_ms": 2572.921299841255,
        "cost_usd": 0.0013134000000000002,
        "total_tokens": 13134,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 (Network and Information Systems) have overlapping cybersecurity requirements for high-risk AI systems, particularly those used by essential entities. Here's how they interact:\n\n1. **AI Act's High-Risk AI Requirements**: The AI Act mandates that high-risk AI systems, as defined in Annex III, must comply with specific requirements (Art. 9-15) to ensure they are safe and secure. This includes conducting conformity assessments, obtaining CE marking, and keeping records of these assessments.\n\n2. **NIS2's Essential Entities Security Measures**: NIS2, which applies to essential entities, mandates that these entities must have in place security measures to protect their networks and information systems. This includes having a security incident management plan, ensuring the security of network and information systems, and having a risk management process.\n\n3. **Interaction**: High-risk AI systems used by essential entities must meet both sets of requirements. Specifically, these systems must comply with the AI Act's high-risk AI requirements, which include the conformity assessment and CE marking. Additionally, they must also comply with NIS2's security measures for essential entities. This means that the AI system must not only be secure according to the AI Act's requirements but also meet the broader security standards expected",
        "exact_match": 0.0,
        "f1": 0.28873239436619713,
        "latency_ms": 3331.4217999577522,
        "cost_usd": 0.0013176000000000002,
        "total_tokens": 13176,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 interacts with the AI Act's data governance requirements in several ways, creating a complex interplay between the two regulations:\n\n1. **Data Protection by Design**: The GDPR's data protection by design principle requires that data protection should be integrated into the design of products and services. This principle aligns with the AI Act's emphasis on data governance, as both aim to ensure that data is handled securely and transparently. For AI systems, this often means incorporating data protection measures from the outset of the development process.\n\n2. **Art. 22 of the GDPR**: Article 22 of the GDPR, which prohibits automated individual inferences based on personal data and requires human intervention, intersects with the AI Act's requirements. AI systems that make automated decisions about individuals, particularly in high-risk categories like employment, workers management, and access to self-employment (Annex III Category 4), must comply with the GDPR's data protection by design principles. This can lead to additional requirements for transparency, human oversight, and the right to challenge decisions.\n\n3. **Art. 25 of the GDPR**: Article 25 of the GDPR, which requires organizations to conduct a data protection impact assessment (DPIA) for high-risk processing",
        "exact_match": 0.0,
        "f1": 0.3754512635379061,
        "latency_ms": 3203.3200999721885,
        "cost_usd": 0.0013175,
        "total_tokens": 13175,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 (deployer) uses a third-party high-risk AI system, the supply chain implications under the AI Act and DORA are significant and interconnected. Here's a breakdown of the key points:\n\n1. **AI Act Compliance**:\n   - **GPAI Model Provider**: The GPAI model provider is responsible for ensuring that the model and documentation comply with the AI Act requirements. They are also responsible for providing a model card, ensuring copyright compliance, and summarizing the training data.\n   - **AI System Provider (Fintech)**: The AI system provider is responsible for integrating the model, ensuring compliance with the AI Act's requirements (Art. 9-15), and conducting conformity assessment.\n   - **Deployer (Financial Institution)**: The deployer is responsible for using the system, ensuring human oversight, monitoring the system's performance, and reporting incidents. They must also contractually ensure access to audit the AI system, incident notification, data localization, and an exit strategy.\n\n2. **DORA Compliance**:\n   - **Third-Party ICT Risk Management**: The third-party (AI system provider) must manage the risks associated with the use of the AI system within the financial sector. This includes conducting third-party risk management (Art",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 3148.1135999783874,
        "cost_usd": 0.0013165,
        "total_tokens": 13165,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 (Data Protection and Rights of Individuals Directive) have different but overlapping requirements for credit scoring AI. Here's how they interact:\n\n1. **AI Act Requirements**: For high-risk AI systems, the AI Act mandates full compliance with its high-risk requirements (Art. 9-15), including conformity assessment and CE marking. This is applicable to any AI system that falls under the AI Act's high-risk categories, including credit scoring systems.\n\n2. **DORA Requirements**: DORA, specifically for credit scoring AI (Annex III Category 5b), requires compliance with several additional requirements:\n   - **ICT Risk Management**: DORA mandates that the AI system provider must conduct an ICT risk management process (Art. 28-30), including third-party risk management, contractual safeguards, and exit strategies.\n   - **Incident Reporting**: The AI system provider must have a process for incident reporting (Art. 17-19).\n   - **Resilience Testing**: The AI system provider must conduct resilience testing (Art. 24-27).\n\n3. **Interaction**: While the AI Act provides the overarching framework for high-risk AI systems, DORA adds specific requirements that are particularly relevant to credit scoring AI",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 3178.5856001079082,
        "cost_usd": 0.0013151000000000003,
        "total_tokens": 13151,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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, both the AI Act and NIS2 require dual incident notification. Specifically, under the AI Act, the AI system provider must notify the competent authority and the GPAI model provider of the breach. Under NIS2, the entity responsible for the AI system (the deployer) must notify the relevant national authority. \n\nThis dual requirement ensures that both the technical aspects of the AI system and the broader impact on essential infrastructure are addressed. The AI Act focuses on the AI system itself and its compliance with high-risk AI requirements, while NIS2 addresses the broader implications on critical infrastructure and the need for swift notification to authorities.",
        "exact_match": 0.0,
        "f1": 0.27322404371584696,
        "latency_ms": 2307.0334000512958,
        "cost_usd": 0.00131,
        "total_tokens": 13100,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 with the AI Act's requirements. Here are the key points to consider:\n\n1. **Compliance with AI Act Requirements**: The AI Act mandates a conformity assessment for high-risk AI systems. This conformity assessment is designed to verify that the AI system meets the requirements of the AI Act, including the high-risk AI requirements (Art. 9-15). This ensures that the AI system is not only technically compliant but also meets the essential safety and security requirements of the AI Act.\n\n2. **Integration with Existing Safety Legislation**: The conformity assessment process is designed to be integrated with existing product safety legislation. This integration is intended to avoid duplication and to ensure that the conformity assessment process is efficient and effective. The conformity assessment process should be able to identify and address any safety-related issues that may arise from the AI system, ensuring that the AI system is safe to use.\n\n3. **Harmonization Efforts**: The AI Act includes harmonization efforts to ensure that the conformity assessment process is consistent across different Member States. This harmonization is crucial to ensure that AI systems are assessed in a way that is consistent with",
        "exact_match": 0.0,
        "f1": 0.28975265017667845,
        "latency_ms": 3147.871599998325,
        "cost_usd": 0.0013194,
        "total_tokens": 13194,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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\n\n#### Regulatory Frameworks and Obligations\n\n1. **AI Act (Art. 9-15, 25, 26, 43-49)**\n   - **Obligations**: The bank is a high-risk AI system provider and must complete conformity assessment, draw up EU declaration of conformity, and register the system in the EU database.\n   - **Timeline**: Completion of conformity assessment within 12 months of the AI system's deployment.\n   - **Authorities**: The notified body, EU Commission, and national authorities.\n   - **Potential Penalties**: EUR 15 million/3% of annual global turnover (Art. 79-82), additional penalties under Art. 99 for safety-critical systems.\n\n2. **GDPR (Art. 22, 25, 35, 28-30)**\n   - **Obligations**: The bank must ensure transparency, provide explanations, and ensure human oversight. It must also conduct a DPIA and ensure third-party risk management.\n   - **Timeline**: Conducting DPIA within 12 months of the AI system",
        "exact_match": 0.0,
        "f1": 0.2681992337164751,
        "latency_ms": 3305.709700100124,
        "cost_usd": 0.0013127,
        "total_tokens": 13127,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 that the energy utility deploys for grid management, the following obligations apply under the AI Act, DORA, and NIS2:\n\n- **AI Act**: \n  - The third-party provider, who substantially modified the model, would be considered a provider under Article 25 of the AI Act. Therefore, they would be responsible for ensuring compliance with the requirements of the AI Act, including conducting conformity assessment, drawing up the EU declaration of conformity, and registering the system in the EU database.\n  - The energy utility would be considered a deployer and would be responsible for ensuring human oversight, monitoring the performance of the system, and reporting incidents. They would also need to ensure compliance with the requirements of DORA, such as third-party risk management, contractual safeguards, and exit strategies.\n\n- **DORA**:\n  - The energy utility would be a deployer and would need to ensure compliance with DORA's requirements, including ICT risk management, third-party risk management, incident reporting, and resilience testing.\n\n- **NIS2**:\n  - The energy utility, as an essential entity under NIS2, would be subject to the requirements of NIS2, such as ensuring the security of critical infrastructure, incident reporting, and resilience testing",
        "exact_match": 0.0,
        "f1": 0.34265734265734266,
        "latency_ms": 3223.5588002949953,
        "cost_usd": 0.0013182,
        "total_tokens": 13182,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 addressing the AI Act, GDPR, NIS2, and fundamental rights. Here's a structured approach to ensure all key regulations are covered, along with the key tensions between these frameworks:\n\n### 1. **Compliance Matrix for AI Diagnostic System**\n\n#### **1.1 AI Act Compliance**\n- **High-Risk Use Case**: The AI diagnostic system falls under Annex III Category 2 (Management and operation of critical infrastructure).\n- **Requirements**: Full high-risk requirements (Art. 9-15), conformity assessment, CE marking.\n- **Implementation**: Conduct conformity assessment, draft EU declaration of conformity, register system in EU database, affix CE marking.\n- **Tension**: The AI Act's high-risk requirements can be stringent and may conflict with GDPR's privacy and fundamental rights protections. For example, the AI Act's requirement for transparency and accountability might conflict with GDPR's right to data portability and the right to data erasure.\n\n#### **1.2 GDPR Compliance**\n- **High-Risk Use Case**: The AI diagnostic system falls under Annex III Category 5 (Access to and enjoyment of essential private and public services).\n- **Requirements**: Art. 22 (right",
        "exact_match": 0.0,
        "f1": 0.2641509433962264,
        "latency_ms": 3188.3392999880016,
        "cost_usd": 0.0013172000000000001,
        "total_tokens": 13172,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 fines associated with each regulation and the fact that the company may face penalties for multiple violations within a single regulation.\n\n1. **Prohibited AI Practices (AI Act):** The AI Act imposes a fine of up to 3% of the company's annual global turnover for violations of prohibited AI practices. For a tech company, this could be a significant fine.\n\n2. **GDPR Data Protection Violations:** The GDPR imposes a fine of up to 4% of the company's annual global turnover or \u20ac20 million, whichever is higher, for violations of data protection requirements. Again, this could be a substantial fine.\n\n3. **NIS2 Cybersecurity Breach:** The NIS2 imposes a fine of up to 10% of the company's annual global turnover for cybersecurity breaches. This is also a substantial fine.\n\n4. **GPAI Model Systemic Risk:** The GPAI model, if deemed high-risk, may be subject to additional fines or penalties under the AI Act. However, the AI Act does not specify a separate",
        "exact_match": 0.0,
        "f1": 0.3157894736842105,
        "latency_ms": 3166.462100110948,
        "cost_usd": 0.0013186,
        "total_tokens": 13186,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 involves a multi-layered approach. Here's a structured way to handle this:\n\n### 1. **Identify High-Risk AI Systems**\n   - **AI Act Compliance**: Classify the AI recruitment system as a high-risk system based on Annex III of the AI Act. This involves assessing whether the system processes sensitive data, operates critical infrastructure, or makes automated decisions that significantly affect individuals.\n   - **GDPR Compliance**: Ensure the system complies with GDPR, especially Art. 22 (right not to be subject to automated decision-making), Art. 25 (data minimization), and Art. 28 (data protection impact assessments (DPIAs) for high-risk systems).\n   - **NIS2 Compliance**: Depending on the criticality of the system, ensure compliance with NIS2, which requires certain entities to be covered by the directive. This might include entities operating in the financial sector, critical infrastructure, or other sectors deemed essential for the functioning of society.\n\n### 2. **CE Marking and Registration**\n   - **CE Marking**: Affix the CE marking",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 3208.4009000100195,
        "cost_usd": 0.0013161,
        "total_tokens": 13161,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 law enforcement purposes to prevent an imminent terrorist threat, the law enforcement agency would need to navigate through a complex legal pathway involving multiple regulatory frameworks. Here's a detailed breakdown of the conditions, legal pathways, and oversight requirements across these frameworks:\n\n### Legal Pathway and Conditions\n\n1. **Annex III High-Risk Use Cases**\n   - **Category 1 (Biometric Identification and Categorisation)**: Real-time biometric identification falls under this category, making it a high-risk AI system.\n   - **Annex III Compliance**: The AI system would need to be classified as high-risk and comply with the requirements of Annex III, including conformity assessment, CE marking, and EU declaration of conformity.\n\n2. **AI Act Compliance**\n   - **Full High-Risk Requirements (Art. 9-15)**: The AI system would need to comply with the full requirements of the AI Act, including transparency, accountability, and human oversight.\n   - **Conformity Assessment**: The system would need to undergo conformity assessment and be certified by a notified body.\n   - **CE Marking**: The system must be CE marked, indicating compliance with the AI Act.\n   - **EU Declaration of Conform",
        "exact_match": 0.0,
        "f1": 0.2893890675241158,
        "latency_ms": 3218.249599914998,
        "cost_usd": 0.0013178,
        "total_tokens": 13178,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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": "To trace the full enforcement cascade across the AI Act, GDPR, and potential liability, let's break down the process step by step:\n\n1. **GDPR Compliance Violation**: The data protection authority finds that the training data used for the AI risk assessment system is biased against certain ethnic groups. This bias violates the GDPR's requirement for fairness and non-discrimination in data processing, as outlined in Article 9 (special categories of personal data) and Article 22 (right not to be subject to automated decision-making).\n\n2. **GDPR Enforcement**: Given the violation, the data protection authority will likely issue a notice to the insurance company requiring them to cease processing the biased data and take corrective measures to ensure compliance. They may also demand that the insurance company provide a detailed explanation of how they address the bias and ensure that no discriminatory outcomes occur. If the insurance company fails to comply, they could face fines as outlined in Article 83 of the GDPR.\n\n3. **AI Act Compliance**: The AI Act also requires high-risk AI systems to be compliant with its requirements, including Article 9-15, which mandates high-risk AI systems to be conformant with the requirements of the AI Act and undergo conformity assessment and CE marking. The insurance company's AI risk",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 3172.5690001621842,
        "cost_usd": 0.0013204,
        "total_tokens": 13204,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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, the interaction between the AI Act's quality management system (QMS) requirement and DORA's ICT governance, as well as NIS2's cybersecurity policies, involves several layers of compliance. Here's a breakdown of how these interact:\n\n1. **AI Act Quality Management System (QMS)**: The AI Act requires that high-risk AI systems, including those used for fraud detection, have a QMS. This QMS must comply with the requirements of the AI Act (Chapter III, Section 2). The QMS should ensure that the AI system is developed, maintained, and used in a way that aligns with the principles of the AI Act, including transparency, explainability, and accountability.\n\n2. **DORA ICT Governance**: DORA (Data Protection and Privacy Regulation for the Financial Sector) has its own set of requirements for ICT governance, which can overlap with the AI Act's QMS. For instance, DORA requires that AI systems be developed and used in compliance with data protection laws, such as the GDPR. The QMS from the AI Act can help ensure that the AI system meets these data protection requirements, but it may also need to address additional aspects like transparency and accountability, which are specific",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 3174.4634001515806,
        "cost_usd": 0.0013183000000000001,
        "total_tokens": 13183,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 the AI Act, GDPR, DORA, and NIS2. Here's a comparison of these aspects:\n\n### Incident Reporting Timelines\n\n**AI Act:**\n- **Notification:** Within 24 hours of becoming aware of the incident.\n- **Reporting:** Within 30 days of the incident.\n\n**GDPR:**\n- **Notification:** Within 72 hours of becoming aware of the incident.\n- **Reporting:** Within 30 days of the incident.\n\n**DORA:**\n- **Notification:** Within 24 hours of becoming aware of the incident.\n- **Reporting:** Within 30 days of the incident.\n\n**NIS2:**\n- **Notification:** Within 24 hours of becoming aware of the incident.\n- **Reporting:** Within 30 days of the incident.\n\n### Authorities Involved\n\n**AI Act:**\n- **Notification:** To the AI Act authority.\n- **Reporting:** To the AI Act authority.\n\n**GDPR:**\n- **Notification:** To the supervisory authority.\n- **Reporting:** To the supervisory authority.\n\n**DORA:**\n- **Notification:** To the DORA",
        "exact_match": 0.0,
        "f1": 0.36162361623616235,
        "latency_ms": 3211.804900318384,
        "cost_usd": 0.0013144,
        "total_tokens": 13144,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "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 need to navigate several legal boundaries, exceptions, and framework interactions. Here\u2019s a breakdown of the key considerations:\n\n### Legal Boundaries and Exceptions\n\n1. **Privacy and Data Protection**: The system must comply with data protection laws, particularly the General Data Protection Regulation (GDPR). This includes obtaining informed consent from employees, ensuring data minimization, and ensuring that data is processed lawfully, fairly, and in a transparent manner.\n\n2. **Employment Law**: The system must not discriminate against employees based on protected characteristics (such as gender, race, or disability). It should also comply with the right to privacy and the right to a private life, as outlined in the European Charter of Fundamental Rights.\n\n3. **Workplace Safety**: The system should not pose a risk to the physical or mental health of employees. Any potential risks should be mitigated and documented.\n\n4. **Regulatory Compliance**: Depending on the specific use case, the system might need to comply with sector-specific regulations, such as the General Data Protection Regulation (GDPR), the Law Enforcement Directive (LED), or national employment laws.\n\n### Framework Interactions\n\n1. **GDPR**: The system should undergo a Data Protection",
        "exact_match": 0.0,
        "f1": 0.25,
        "latency_ms": 3192.703199572861,
        "cost_usd": 0.0013186,
        "total_tokens": 13186,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      }
    ]
  },
  "cognigraph-pcst": {
    "summary": {
      "method": "cognigraph-pcst",
      "dataset": "MultiGov-30",
      "n_questions": 20,
      "avg_em": 0.0,
      "avg_f1": 0.2654354182405843,
      "avg_latency_ms": 177090.95879504457,
      "avg_cost_usd": 9.600000000000003e-05,
      "avg_tokens": 960.0,
      "avg_rounds": 2.75,
      "avg_nodes": 8.0,
      "total_cost_usd": 0.0019200000000000005,
      "total_latency_s": 3541.8191759008914
    },
    "questions": [
      {
        "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": "### Synthesized Answer\n\nThe EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons through their intersection and overlap in provisions related to automated decision-making. Both regulations address the issue from different angles but aim to protect individuals from the negative impacts of automated decision-making.\n\n#### Key Areas of Agreement\n\n1. **Prohibition of Specific Practices**: The EU AI Act, specifically Article 5(1)(f), prohibits the use of emotion recognition in workplace and educational institutions. This aligns with GDPR's broader prohibition of automated decision-making in Article 22, which requires that automated decision-making be necessary for entering into a contract, authorized by law, or based on the individual's explicit consent.\n\n2. **Cumulative Liability Exposure**: Evidence Chunk [4] highlights that systems violating the EU AI Act's Article 5(1)(f) are likely to also violate multiple GDPR provisions, such as Article 22. This indicates a cumulative liability exposure, suggesting that the intersection between the two regulations is significant.\n\n#### Conflicts and Contradictions\n\n1. **Specific Prohibition vs. General Prohibition**: Agent: aiact_art5_prohibited asserts that the EU AI Act specifically prohibits emotion recognition, while GDPR addresses the broader issue of automated decision-making. This difference in focus creates a potential conflict, as the EU AI Act provides specific prohibitions, whereas GDPR addresses the issue more broadly.\n\n#### Synthesized Answer\n\nThe EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons through their intersection and overlap in provisions related to automated decision-making. The EU AI Act, specifically Article 5(1)(f), prohibits the use of emotion recognition in specific contexts, such as workplace and educational institutions. GDPR, on the other hand, addresses the broader issue of automated decision-making, prohibiting automated decision-making based on certain conditions (e.g., necessity for entering into a contract, authorization by law, or explicit consent). Evidence Chunk [4] highlights the cumulative liability exposure for systems that violate the EU AI Act's Article 5(1)(f), suggesting that such systems likely also violate multiple GDPR provisions, such as Article 22. This intersection of the two regulations underscores their complementary nature in protecting individuals from the negative impacts of automated decision-making.\n\n### Overall Confidence\nBased on the provided evidence and the intersection of the EU AI Act and GDPR, the overall confidence in this reasoning is **95%**.\n\n### Key Evidence Sources\n1. **Evidence Chunk [2]**: Provides additional context on the intersection between the EU AI Act and GDPR, highlighting the cumulative liability exposure for systems violating the EU AI Act.\n2. **Evidence Chunk [4]**: Highlights the cumulative liability exposure for systems violating the EU AI Act, reinforcing the interconnected nature of the two regulations.",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 89268.20529997349,
        "cost_usd": 0.00010210000000000002,
        "total_tokens": 1021,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nWhen a high-risk AI system violates both the EU AI Act and the GDPR, the combined penalty exposure is up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. This conclusion is based on the specific prohibition of social scoring under the AI Act (Article 5(1)(c)) and the cumulative impact of this violation with GDPR provisions such as Art. 22 (automated decision-making) and Art. 5 (data processing principles).\n\n### Key Evidence Sources\n1. [1] - Enforcement details for penalties under the AI Act.\n2. [2] - Specific prohibition of social scoring under the AI Act and its intersection with GDPR provisions.\n\n### Areas of Agreement\n- Both agents agree that the AI Act's prohibition on social scoring is a significant violation.\n- Both agents agree that the GDPR's provisions related to automated decision-making and data processing principles intersect with the AI Act's prohibition.\n- Both agents agree that the AI Act's maximum penalty (EUR 35 million or 7% of worldwide annual turnover, whichever is higher) is the higher of the two penalties.\n\n### Flagged Contradictions\n- There are no contradictions between the agents' outputs.\n\n### Confidence Level\nBased on the provided evidence and the specific guidance given, the confidence level is 100%.\n\n### Overall Confidence\n100%\n\n### Synthesized Conclusion\nWhen a high-risk AI system violates both the EU AI Act and the GDPR, the combined penalty exposure is up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. This reflects the severity of the violations and the cumulative impact of the prohibitions under both the AI Act and the GDPR.",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 57046.36809974909,
        "cost_usd": 7.72e-05,
        "total_tokens": 772,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Key Areas of Agreement Across Agents\n\n1. **Fundamental Rights Impact Assessment (AI Act)**: All agents agree that a fundamental rights impact assessment is necessary for AI systems that fall under Article 5 of the AI Act. This includes systems that use subliminal techniques, exploit vulnerabilities, or engage in prohibited practices such as social scoring, real-time remote biometric identification, and emotion recognition in workplace and educational institutions.\n\n2. **Data Protection Impact Assessment (GDPR)**: All agents agree that a data protection impact assessment is necessary for AI systems that use social scoring, as they may violate GDPR Article 22 (automated decision-making) and Article 5 (data processing principles).\n\n#### Areas of Disagreement\n\n1. **Classification of AI Systems**: There is some disagreement on how to classify AI systems according to Article 5 categories. Some agents suggest that all AI systems should be classified, while others argue that only systems that use prohibited practices need to be classified.\n\n2. **Scope of Data Protection Impact Assessment**: Some agents suggest that a data protection impact assessment should be conducted for all AI systems that use social scoring, while others argue that only systems that use social scoring for commercial purposes need to be assessed.\n\n### Synthesized Compliance Steps\n\n1. **Inventory All AI Systems**: Conduct a thorough inventory of all AI systems to identify those that could be classified under Article 5. This includes systems that use subliminal techniques, exploit vulnerabilities, or engage in prohibited practices such as social scoring, real-time remote biometric identification, and emotion recognition in workplace and educational institutions.\n\n2. **Classify Against Art. 5 Categories**: Classify each AI system according to Article 5 categories. This will help identify which systems require a fundamental rights impact assessment and which require a data protection impact assessment.\n\n3. **Identify Emotion Recognition in Workplace/Education**: Review any emotion recognition systems in workplace and educational institutions. These systems must be reviewed for compliance with Article 5 and GDPR.\n\n4. **Review Biometric Processing Activities**: Review all biometric processing activities to ensure they comply with Article 5. This includes biometric categorisation using sensitive characteristics.\n\n5. **Check for Dark Patterns/Manipulative AI**: Identify any systems that use subliminal techniques or manipulative or deceptive techniques. These systems must be reviewed for compliance with Article 5 and GDPR.\n\n### Compliance with Both Regulations\n\n- **Fundamental Rights Impact Assessment**: Conduct a fundamental rights impact assessment for AI systems that fall under Article 5 categories. This includes systems that use subliminal techniques, exploit vulnerabilities, or engage in prohibited practices such as social scoring, real-time remote biometric identification, and emotion recognition in workplace and educational institutions. The assessment should evaluate the potential impact on fundamental rights and propose measures to mitigate any adverse effects.\n\n- **Data Protection Impact Assessment**: Conduct a data protection impact assessment for AI systems that use social scoring. This assessment should evaluate the potential impact on individuals' rights and freedoms and propose measures to mitigate any adverse effects.\n\n### Overall Confidence\nThe overall confidence in the synthesized answer is 80%. The consensus among the agents on the necessity of fundamental rights impact assessments and data protection impact assessments provides a strong foundation for compliance. However, there is some room for interpretation regarding the classification of AI systems and the scope of data protection impact assessments.\n\n### Key Evidence Sources\n- [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n- [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n- [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n- [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n- [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n\nThe confidence in the evidence sources is 50%, as they are based on assertions from multiple agents.",
        "exact_match": 0.0,
        "f1": 0.14285714285714285,
        "latency_ms": 1917987.2639002278,
        "cost_usd": 0.00012020000000000001,
        "total_tokens": 1202,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesis of Agent Outputs\n\n**Agreement:**\n- Both the AI Act (Article 5) and DORA are separate pieces of legislation that do not directly address dual obligations or incident reporting mechanisms for AI-related incidents.\n\n**Contradictions:**\n- The AI Act focuses on prohibited AI practices, penalties, and the prohibition of certain AI systems.\n- DORA addresses data protection and online rights, but does not mention any specific provisions related to AI-related incidents or dual obligations.\n\n### Synthesized Answer\n\nThe query seeks to understand the dual obligations that apply when a financial entity reports an AI-related incident under both the AI Act and DORA. However, the provided information does not offer specific guidance on this topic. The AI Act (Article 5) focuses on prohibited AI practices, penalties, and the prohibition of certain AI systems, while DORA addresses data protection and online rights. Neither piece of legislation provides specific provisions related to dual obligations or incident reporting mechanisms for AI-related incidents.\n\nTherefore, based on the available information, there are no specific dual obligations or incident reporting mechanisms that apply under both the AI Act and DORA for financial entities reporting AI-related incidents.\n\n### Overall Confidence\n- Confidence: 0%\n\n### Key Evidence Sources\n- AI Act (Article 5)\n- DORA (Data Protection and Rights Online Act)",
        "exact_match": 0.0,
        "f1": 0.23611111111111108,
        "latency_ms": 81503.20829963312,
        "cost_usd": 9.510000000000001e-05,
        "total_tokens": 951,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nBased on the analysis provided by the multiple specialized agents, there is no direct interaction or overlap between the AI Act's cybersecurity requirements for high-risk AI and the NIS2 (Network and Information Systems) security measures for essential entities. The AI Act focuses on the ethical and safety aspects of AI systems, particularly in terms of their impact on individuals and society, while NIS2 aims to ensure the security of network and information systems, especially in critical infrastructures and essential services.\n\n#### Key Evidence Sources:\n1. [aiact_art5_prohibited] - This source provides information on penalties for prohibited AI practices under the AI Act but does not mention cybersecurity requirements or interactions with NIS2.\n2. [1] - This source discusses the prohibition of social scoring by public authorities under the AI Act but does not relate to cybersecurity measures.\n3. [3] - This source outlines the timeline and steps for implementing the AI Act, including auditing AI systems and identifying prohibited practices, but does not address interactions with NIS2.\n4. [4] - This source cross-references with GDPR and the Charter of Fundamental Rights but does not provide information on interactions with NIS2.\n\n#### Conflicts and Contradictions:\n- None of the agents provide information on interactions or overlaps between the AI Act's cybersecurity requirements for high-risk AI and NIS2 security measures.\n\n#### Synthesis:\nThe AI Act and NIS2 address different aspects of AI and cybersecurity. The AI Act focuses on the ethical and safety aspects of AI systems, particularly in terms of their impact on individuals and society, while NIS2 aims to ensure the security of network and information systems, especially in critical infrastructures and essential services. There is no specific interaction or overlap between the AI Act's cybersecurity requirements for high-risk AI and NIS2 security measures for essential entities.\n\n### Overall Confidence: 100%\n\nBased on the analysis and the provided evidence, the confidence in the synthesized answer is 100%.",
        "exact_match": 0.0,
        "f1": 0.18324607329842932,
        "latency_ms": 84920.27650028467,
        "cost_usd": 9.410000000000001e-05,
        "total_tokens": 941,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nThe GDPR's data protection by design principle interacts with the AI Act's data governance requirements in several ways, particularly regarding the prohibition of AI practices that exploit vulnerabilities of specific groups. Both principles aim to ensure that data is processed in a manner that respects individuals' rights and dignity.\n\n#### Key Areas of Agreement:\n1. **Prohibition of Exploitative Practices**: The AI Act's Article 5 prohibits certain AI practices, such as those that exploit vulnerabilities of specific groups (e.g., age, disability, or social/economic situations). This prohibition aligns with the GDPR's data protection by design principle, which requires that data protection should be integrated into the design and functioning of systems from the very beginning.\n2. **Compliance with Article 5 and Article 25**: A system that complies with the AI Act's Article 5, which prohibits the exploitation of vulnerabilities, is likely to also meet the requirements of the GDPR's data protection by design principle (Article 25 of the GDPR). This is because both principles emphasize the importance of integrating data protection into the design of systems.\n\n#### Key Evidence:\n- [2] states that private companies using social scoring for commercial purposes may not be directly caught by Article 5 of the AI Act but could violate GDPR Article 22 (automated decision-making) and Article 5 (data processing principles). This indicates that the AI Act's prohibition of social scoring aligns with the GDPR's data protection by design principle, as both aim to ensure that data is processed in a way that respects individuals' rights and dignity.\n\n#### Synthesis:\nThe AI Act's Article 5 and the GDPR's data protection by design principle intersect in their focus on preventing the misuse of data and ensuring that data is processed in a manner that respects individuals' rights and dignity. Compliance with Article 5 of the AI Act is likely to result in compliance with the GDPR's data protection by design principle.\n\n### Overall Confidence: 95%\n\n#### Key Evidence Sources:\n- [2] (Reference not provided in the original outputs, but should be considered a valid source for the provided information)",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 62280.776099767536,
        "cost_usd": 5.67e-05,
        "total_tokens": 567,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Supply Chain Implications Considering the AI Act\n\n[Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\nWhen a financial institution uses a third-party high-risk AI system, the supply chain implications are significant, considering both the AI Act and DORA (Data-Driven Operations and Regulatory Automation). Specifically, the AI Act prohibits certain AI practices, such as social scoring, which could be enabled by a third-party high-risk AI system. If a financial institution uses a third-party AI system that is used for social scoring, it would be in violation of Article 5(1)(c) of the AI Act. This means that the financial institution could be held liable for the prohibited AI practices, even if they are not directly using the system themselves. The implementation timeline for the prohibitions under the AI Act is from 2 February 2025, which creates a significant challenge for financial institutions that rely on third-party AI systems, as they would need to ensure that their systems are compliant with the AI Act.\n\n#### Supply Chain Implications Considering DORA\n\n[Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n[Agent: dora_enforcement_penalties | Type: assertion | Confidence: 50%]\n[Agent: dora_intersection_with_gdpr | Type: assertion | Confidence: 50%]\n[Agent: dora_intersection_with_charter_of_fundamental_rights | Type: assertion | Confidence: 50%]\n\nThe enforcement penalties for prohibited AI practices under the AI Act are up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. This high-stakes enforcement could lead to significant financial penalties for financial institutions that use third-party high-risk AI systems, which could ripple through the supply chain. Financial institutions might face pressure to demand compliance from their third-party providers, leading to increased scrutiny and potential changes in the way they engage with these providers. The prohibitions under the AI Act intersect with GDPR provisions, such as GDPR Art. 9 (special categories of data), Art. 22 (right not to be subject to solely automated decisions), and the Charter of Fundamental Rights Art. 1 (human dignity), Art. 7 (private life), Art. 8 (data protection). This intersection creates cumulative liability exposure for financial institutions that use third-party high-risk AI systems. For example, if a third-party AI system violates the AI Act, it could also be in violation of GDPR, leading to additional regulatory scrutiny and potential penalties.\n\n### Confidence Level\nBased on the provided evidence, my confidence in this reasoning is 95%. The evidence supports the significant implications for financial institutions that use third-party high-risk AI systems, including the potential for significant financial penalties and the need for compliance with both the AI Act and GDPR.\n\n### Key Evidence Sources\n1. [Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%]\n2. [Agent: dora_enforcement_penalties | Type: assertion | Confidence: 50%]\n3. [Agent: dora_intersection_with_gdpr | Type: assertion | Confidence: 50%]\n4. [Agent: dora_intersection_with_charter_of_fundamental_rights | Type: assertion | Confidence: 50%]",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 101890.52740018815,
        "cost_usd": 0.00012080000000000001,
        "total_tokens": 1208,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nThe AI Act's risk management requirements for social scoring align with DORA ICT risk management principles for credit scoring AI. Both emphasize the importance of transparency, accountability, and the protection of individuals' rights. The AI Act specifically prohibits the use of AI systems for social scoring by or on behalf of public authorities, which is a common practice in credit scoring. This prohibition is consistent with DORA ICT risk management principles, which also focus on ensuring that AI systems do not make decisions that would otherwise be made by humans and that individuals are aware of and have the ability to challenge such decisions. Additionally, the AI Act includes significant penalties for violations, such as fines up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. This aligns with DORA ICT risk management, which also emphasizes the importance of compliance with regulations to avoid significant financial penalties.\n\n### Key Evidence Sources\n1. [aiact_art5_prohibited] - Provides a clear example of how the AI Act's prohibition on social scoring intersects with GDPR principles, such as the right not to be subject to solely automated decisions (GDPR Article 22).\n2. [1] - Indicates significant penalties for violating the AI Act, including fines up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher.\n\n### Confidence\n95%\n\n### Summary\nThe AI Act's prohibition on social scoring by public authorities is consistent with DORA ICT risk management principles, particularly the emphasis on transparency, accountability, and the protection of individuals' rights. The risk management requirements of the AI Act are therefore consistent with DORA ICT risk management for credit scoring AI.",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 63846.696000080556,
        "cost_usd": 6.630000000000001e-05,
        "total_tokens": 663,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nBased on the provided evidence chunks, there is no specific information regarding dual incident notification requirements when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2. The evidence focuses on the AI Act's prohibitions on certain AI practices, enforcement penalties, implementation timelines, and cross-regulation with other data protection laws. There is no mention of incident notification requirements for cybersecurity breaches affecting essential infrastructure under both the AI Act and NIS2.\n\n### Areas of Agreement\n- No specific information exists regarding dual incident notification requirements for the specified scenario.\n\n### Contradictions\n- There are no contradictions between the agents' outputs as each focuses on different aspects of the AI Act and NIS2, with no overlap in the specific information provided.\n\n### Synthesis\nGiven the lack of specific information regarding dual incident notification requirements for the specified scenario, it is not possible to provide a focused reasoning response. The available evidence does not cover this specific intersection of regulations.\n\n### Overall Confidence\n0%\n\n### Key Evidence Sources\n- Evidence chunks related to the AI Act's prohibitions on certain AI practices, enforcement penalties, implementation timelines, and cross-regulation with other data protection laws.\n\n### Final Response\nThere is no specific information regarding dual incident notification requirements when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2. The available evidence focuses on the AI Act's prohibitions and cross-regulation with other data protection laws, but does not cover incident notification requirements for cybersecurity breaches affecting essential infrastructure under both the AI Act and NIS2.",
        "exact_match": 0.0,
        "f1": 0.17391304347826086,
        "latency_ms": 44028.20080006495,
        "cost_usd": 4.1700000000000004e-05,
        "total_tokens": 417,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesis of Agent Outputs\n\n#### Areas of Agreement\n1. **Interactions with Existing Legal Frameworks**: Multiple agents agree that the AI Act's prohibitions on certain AI practices intersect with existing legal frameworks such as the General Data Protection Regulation (GDPR) and the Charter of Fundamental Rights.\n2. **Conformity Assessment**: Agents agree that organizations must conduct conformity assessments to ensure their AI systems comply with the AI Act's prohibitions, which may necessitate compliance with other existing legal frameworks.\n3. **Timeline for Implementation**: Agents agree that the implementation timeline for the AI Act begins from 2 February 2025, requiring organizations to conduct audits of their existing AI systems to identify practices that violate Article 5.\n\n#### Key Evidence Sources\n1. [Agent: aiact_art5_prohibited | Type: synthesis | Confidence: 95%]\n2. [2] (not specified in the given text, but implied as a clear example)\n3. [3] (not specified in the given text, but implied as outlining the implementation timeline)\n\n#### Contradictions\nThere are no direct contradictions identified between the agents' outputs.\n\n### Synthesized Answer\n\nThe AI Act's conformity assessment for AI-embedded products does not directly interact with existing product safety legislation, but it intersects with existing legal frameworks such as the General Data Protection Regulation (GDPR) and the Charter of Fundamental Rights. This intersection is significant because the AI Act's prohibitions on certain AI practices are closely aligned with the GDPR's principles and the Charter of Fundamental Rights' provisions.\n\nSpecifically, Article 5(1)(c) of the AI Act prohibits social scoring by public authorities or on their behalf. This prohibition aligns with GDPR Article 22, which prohibits automated decision-making based on personal data that could affect a person's fundamental rights, such as their right to privacy and dignity. Therefore, any AI system that violates Article 5(1)(c) is likely to also violate GDPR Article 22.\n\nThe implementation timeline for the AI Act begins from 2 February 2025. Organizations must conduct an audit of their existing AI systems to identify any practices that violate Article 5. This includes identifying and decommissioning systems that violate the prohibitions. This process aligns with the need for conformity assessment, as organizations must ensure their AI systems comply with the AI Act's prohibitions, which in turn may necessitate compliance with other existing legal frameworks.\n\nIn summary, while the AI Act does not directly interact with existing product safety legislation, its prohibitions on certain AI practices intersect with existing legal frameworks such as GDPR and the Charter of Fundamental Rights. Organizations must conduct conformity assessments to ensure their AI systems comply with the AI Act's prohibitions, which may necessitate compliance with other existing legal frameworks. This intersection creates a cumulative liability exposure for organizations that violate the AI Act's prohibitions.\n\n### Overall Confidence\nThe confidence level in the synthesized answer is 95%, based on the consensus among the agents and the clear evidence provided in their outputs.\n\n### Confidence Breakdown\n- **Areas of Agreement**: 100%\n- **Contradictions**: 0%\n- **Overall Confidence**: 95%\n\n### Final Answer\nThe AI Act's conformity assessment for AI-embedded products does not directly interact with existing product safety legislation, but it intersects with existing legal frameworks such as the General Data Protection Regulation (GDPR) and the Charter of Fundamental Rights. Specifically, the AI Act's prohibitions on certain AI practices align with GDPR Article 22, which prohibits automated decision-making based on personal data that could affect a person's fundamental rights. Organizations must conduct conformity assessments to ensure their AI systems comply with the AI Act's prohibitions, which may necessitate compliance with other existing legal frameworks. This intersection creates a cumulative liability exposure for organizations that violate the AI Act's prohibitions.",
        "exact_match": 0.0,
        "f1": 0.12995245641838354,
        "latency_ms": 57834.70389991999,
        "cost_usd": 6.48e-05,
        "total_tokens": 648,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Regulatory Obligations, Timelines, Authorities, and Potential Penalties\n\n#### Regulatory Obligations\n\n1. **Article 5 Prohibited AI Practices (EU AI Act)**: The bank's credit scoring AI system must be audited against Article 5 of the EU AI Act. Specifically, the system must be checked for prohibited practices such as the use of subliminal techniques, exploitation of vulnerabilities, social scoring, real-time remote biometric identification, and emotion recognition in workplace and educational institutions. If any of these prohibited practices are found, the system must be decommissioned immediately.\n\n2. **GDPR Art. 22 (Automated Decision-Making)**: The bank's credit scoring AI system may also be subject to GDPR Art. 22, which prohibits the use of automated decision-making solely based on automated processing when it is necessary to make a decision that significantly affects a person. This could apply if the AI system makes decisions that significantly affect a large number of customers, such as credit scoring, without human oversight or intervention.\n\n3. **GDPR Art. 9 (Special Categories of Data)**: The bank's credit scoring AI system may also be subject to GDPR Art. 9, which regulates the processing of special categories of personal data (including biometric data). If the credit scoring system processes biometric data, it must comply with GDPR Art. 9, including obtaining explicit consent and ensuring data minimization and security.\n\n4. **GDPR Art. 5 (Data Processing Principles)**: The bank's credit scoring AI system must comply with GDPR Art. 5, which requires that personal data be processed lawfully, fairly, and in a transparent manner. This includes ensuring that the data is necessary for the specific purposes of the credit scoring system and that it is stored securely.\n\n#### Timelines and Authorities\n\n1. **Implementation Timeline**: The prohibitions under Article 5 of the EU AI Act apply from 2 February 2025. The bank must conduct an audit of its AI systems by this date to identify any prohibited practices. Any systems found to violate Article 5 must be decommissioned immediately.\n\n2. **National Market Surveillance Authorities**: National market surveillance authorities are responsible for enforcing the prohibitions under Article 5. The AI Office will coordinate enforcement at the EU level.\n\n#### Potential Penalties\n\n1. **EU AI Act Art. 99(3)**: The bank could face penalties for deploying prohibited AI practices. The maximum penalty for deploying prohibited AI practices is up to EUR 20 million or 4% of the total worldwide annual turnover of the undertaking, whichever is higher.\n\n### Key Evidence Sources\n\n1. **EU AI Act (Article 5, 99(3))**: Official EU AI Act text and related guidelines.\n2. **GDPR (Articles 22, 9, 5)**: Official GDPR text and related guidelines.\n3. **National Market Surveillance Authorities**: Official enforcement guidelines and enforcement actions.\n\n### Overall Confidence\nThe overall confidence in the synthesized answer is 80%. This confidence is derived from the consensus of the agents' outputs and the alignment of the regulatory obligations, timelines, authorities, and potential penalties across the EU AI Act and GDPR. The contradictions identified (e.g., GDPR Art. 22 and Article 5 of the EU AI Act) are addressed by the unified interpretation of the regulations.\n\n### Final Synthesis\nThe bank's credit scoring AI system must be audited for compliance with Article 5 of the EU AI Act, GDPR Art. 22, GDPR Art. 9, and GDPR Art. 5. Any systems found to violate these provisions must be decommissioned immediately. National market surveillance authorities are responsible for enforcing these prohibitions. The bank could face penalties for deploying prohibited AI practices, with the maximum penalty being up to EUR 20 million or 4% of the total worldwide annual turnover of the undertaking, whichever is higher.",
        "exact_match": 0.0,
        "f1": 0.19383259911894274,
        "latency_ms": 103310.01339992508,
        "cost_usd": 0.00010130000000000001,
        "total_tokens": 1013,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Areas of Agreement\n1. **Primary Obligations**: The energy utility bears the primary obligations under the AI Act (Article 5), NIS2, and DORA.\n2. **Third-Party Provider Responsibility**: The third-party provider is responsible for ensuring that any modifications to the AI model comply with the AI Act.\n3. **Article 5 Violation Liability**: If the modifications violate Article 5, the energy utility would be held liable for the violation under Article 5.\n\n#### Contradictions\n1. **Agent: aiact_art5_prohibited** states that the energy utility bears the primary obligations under the AI Act (Article 5), while the third-party provider is responsible for ensuring that any modifications comply with the AI Act. This contradicts the assertion that the third-party provider is solely responsible for ensuring compliance with Article 5.\n\n#### Synthesized Answer\nThe energy utility bears the primary obligations under the AI Act (Article 5), NIS2, and DORA. The third-party provider is responsible for ensuring that any modifications to the AI model comply with the AI Act. If the modifications violate Article 5, the energy utility would be held liable for the violation under Article 5.\n\n### Confidence Level\nI am 85% confident in this synthesis. The primary disagreement between the agents is regarding the responsibility of the third-party provider. While one agent asserts that the third-party provider is solely responsible for ensuring compliance with Article 5, another agent states that the energy utility bears the primary obligations. Given the complexity of the directive and the need for a balanced approach, I have weighted the primary obligations on the energy utility and the responsibility on the third-party provider.\n\n### Key Evidence Sources\n1. **AI Act (Article 5)**: Provides specific prohibitions on certain AI practices.\n2. **NIS2**: Requires critical infrastructure operators to ensure the security of their networks and systems.\n3. **DORA**: Aims to ensure that digital services are resilient and secure.\n4. **Article 5 of the AI Act**: Prohibits certain AI practices, including subliminal techniques, social scoring, real-time remote biometric identification, and emotion recognition in workplace or educational settings.\n\n### Overall Confidence\n85%",
        "exact_match": 0.0,
        "f1": 0.18313253012048192,
        "latency_ms": 100553.40439965948,
        "cost_usd": 0.00011540000000000001,
        "total_tokens": 1154,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Comprehensively Addressing Compliance for a Hospital Deploying an AI Diagnostic System\n\n#### Key Tensions Between the AI Act, GDPR, NIS2, and Fundamental Rights\n\n**1. AI Act vs. GDPR:**\n- **Social Scoring and Biometric Data**: The AI Act prohibits the use of social scoring and biometric data in publicly accessible spaces, which can conflict with GDPR's right to data protection and the right not to be subject to solely automated decision-making.\n- **Manipulative AI**: The AI Act's prohibition on manipulative/deceptive techniques aligns with GDPR's right to privacy and the right to data protection.\n\n**2. AI Act vs. NIS2:**\n- **Robust Security Measures**: The AI Act requires robust security measures, which can conflict with NIS2's requirement for cybersecurity measures and incident reporting.\n\n**3. GDPR vs. NIS2:**\n- **Cybersecurity Measures**: GDPR's requirement for data protection measures can conflict with NIS2's requirement for cybersecurity measures.\n\n**4. Fundamental Rights:**\n- **Right to Privacy**: The AI Act's prohibition on manipulative/deceptive techniques aligns with the right to privacy under GDPR.\n- **Right to Data Protection**: The AI Act's prohibition on social scoring and biometric data in publicly accessible spaces aligns with GDPR's right to data protection.\n\n### Compliance Steps\n\n#### 1. **AI Act Compliance**\n- **Audit Existing AI Systems**: Identify and classify all AI systems, including the diagnostic system, according to the AI Act's prohibited practices (e.g., social scoring, biometric categorization, real-time remote biometric identification).\n- **Review Biometric Processing Activities**: Ensure the diagnostic system does not engage in real-time remote biometric identification in publicly accessible spaces. Conduct a risk assessment to determine if any exceptions apply.\n- **Check for Dark Patterns/Manipulative AI**: Ensure the AI diagnostic system does not use subliminal techniques or manipulative/deceptive techniques that could distort informed decision-making. Conduct a thorough review of the system's design and implementation to identify any potential issues.\n\n#### 2. **GDPR Compliance**\n- **Inventory All AI Systems**: Conduct a thorough inventory of all AI systems, including the diagnostic system, and classify them according to the AI Act's prohibited practices.\n- **Classify Against AI Act Categories**: Classify the diagnostic system against the AI Act's categories to identify any prohibited practices.\n- **Review for Manipulative AI**: Ensure the AI diagnostic system does not use subliminal techniques or manipulative/deceptive techniques that could distort informed decision-making. Conduct a thorough review of the system's design and implementation to identify any potential issues.\n\n#### 3. **NIS2 Compliance**\n- **Cybersecurity Measures**: Implement robust cybersecurity measures as required by NIS2 to ensure the AI diagnostic system's security.\n- **Incident Reporting**: Establish a process for incident reporting and response as required by NIS2.\n\n#### 4. **Fundamental Rights Compliance**\n- **Right to Privacy**: Ensure the AI diagnostic system does not use manipulative/deceptive techniques that could infringe on the right to privacy under GDPR.\n- **Right to Data Protection**: Ensure the AI diagnostic system does not use social scoring or biometric data in publicly accessible spaces, which can infringe on the right to data protection under GDPR.\n\n### Overall Confidence\nThe overall confidence in the synthesis of these compliance steps is 80%. The agents' outputs are largely consistent, with minor contradictions noted. The synthesis addresses the key tensions and provides a structured approach to compliance with the AI Act, GDPR, NIS2, and fundamental rights.\n\n### Key Evidence Sources\n- **AI Act**: Official EU guidelines and regulations.\n- **GDPR**: Official EU guidelines and regulations.\n- **NIS2**: Official EU guidelines and regulations.\n- **Fundamental Rights**: Official EU guidelines and regulations.",
        "exact_match": 0.0,
        "f1": 0.1676829268292683,
        "latency_ms": 102967.74840028957,
        "cost_usd": 0.0001134,
        "total_tokens": 1134,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\nThe 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 can be synthesized as follows:\n\n#### 1. AI Act Prohibited Practices\nThe AI Act imposes a maximum fine of up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. Given a turnover of EUR 1 billion, the maximum fine under the AI Act would be:\n- **AI Act Fine**: Up to EUR 35 million\n\n#### 2. GDPR Data Protection Violations\nGDPR imposes fines up to EUR 20 million or 4% of annual global revenue, whichever is higher. Assuming a turnover of EUR 1 billion:\n- **GDPR Fine**: Up to EUR 20 million\n\n#### 3. NIS2 Cybersecurity Breaches\nNIS2 does not provide specific fines for cybersecurity breaches. However, a conservative estimate of EUR 10 million can be considered as a potential upper limit for a cybersecurity breach.\n\n#### Total Maximum Fine Exposure\n- **AI Act Fine**: EUR 35 million\n- **GDPR Fine**: EUR 20 million\n- **NIS2 Fine**: EUR 10 million\n\n**Total Maximum Fine Exposure**: EUR 65 million\n\n### Overall Confidence\nThe overall confidence in this synthesis is 80%. The AI Act and GDPR fines are directly provided, while the NIS2 fine is a conservative estimate based on the lack of specific information.\n\n### Key Evidence Sources\n1. [Agent: aiact_art5_prohibited | Type: contradiction | Confidence: 50%]\n2. [Agent: gdpr_data_protection | Type: contradiction | Confidence: 50%]\n3. [Agent: nis2_cybersecurity | Type: contradiction | Confidence: 50%]\n\n### Final Answer\nThe total maximum fine exposure for the tech company is **EUR 65 million**. This is derived from the maximum fines under the AI Act (EUR 35 million) and GDPR (EUR 20 million), with a conservative estimate of EUR 10 million for NIS2 cybersecurity breaches.",
        "exact_match": 0.0,
        "f1": 0.2902374670184697,
        "latency_ms": 100126.67680019513,
        "cost_usd": 0.00010960000000000001,
        "total_tokens": 1096,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### 1. **AI Act's Territorial Scope and GDPR's Cross-Border Processing Rules**\nThe AI Act's territorial scope is broad, applying to all AI systems placed on the market, put into service, or used within the EU. This aligns with GDPR's cross-border processing rules, which require that personal data be processed in a manner consistent with EU law, including the GDPR, even when processing is carried out outside the EU. Therefore, a multinational should ensure that their AI recruitment system complies with both the AI Act and GDPR, especially since the AI Act's prohibitions on certain AI practices (such as social scoring and real-time remote biometric identification) are likely to be enforced under the AI Act's enforcement mechanism.\n\n#### 2. **Cross-Reference with GDPR Art. 9, Art. 22, and Art. 5**\nGiven that the AI Act's prohibitions on social scoring and certain forms of biometric processing align with GDPR's provisions, a multinational should ensure that their AI recruitment system does not violate these provisions. Specifically, the prohibition on social scoring (Article 5(1)(c)) and the prohibition on certain forms of biometric processing (such as real-time remote biometric identification for law enforcement purposes, unless strictly necessary) should be closely scrutinized.\n\n#### 3. **NIS2 Transposition and Cybersecurity Requirements**\nThe NIS2, which is transposed into national law by Member States, imposes cybersecurity requirements on certain sectors. A multinational should ensure that their AI recruitment system meets these cybersecurity requirements, as failure to do so could result in non-compliance with the NIS2. This includes, but is not limited to, ensuring that the system is resilient against cyber threats, maintains integrity, and ensures availability.\n\n### Specific Steps for Deployment:\n\n1. **Audit Existing AI Systems:**\n   - **Inventory all AI systems:** Conduct a thorough audit to identify any AI systems that could be considered recruitment systems.\n   - **Classify against Art. 5 categories:** Classify the AI systems according to Article 5, ensuring compliance with the prohibitions on subliminal techniques, manipulation, exploitation of vulnerabilities, social scoring, biometric categorisation, real-time remote biometric identification, and other prohibited AI practices.\n   - **Comply with GDPR:** Ensure that the AI systems comply with GDPR, especially when handling personal data, and adhere to the principles of data minimization, purpose limitation, and transparency.\n   - **NIS2 Compliance:** Ensure that the AI systems comply with the NIS2 cybersecurity requirements, including resilience against cyber threats, integrity of data, and availability of services.\n\n### Overall Confidence: 85%\n\n### Key Evidence Sources:\n- **AI Act:** [AI Act](https://ec.europa.eu/justice/data-protection/files/ai/docs/ai_act_en.pdf)\n- **GDPR:** [GDPR](https://gdpr-info.eu/)\n- **NIS2:** [NIS2](https://ec.europa.eu/info/law/laws-enacted/new-eu-directives/national-information-security-directive_en)\n\n### Conclusion:\nA multinational deploying an AI recruitment system across the EU should ensure compliance with the AI Act, GDPR, and NIS2. They should conduct a thorough audit of their AI systems, classify them according to Article 5 of the AI Act, and ensure compliance with GDPR and NIS2 cybersecurity requirements. This will help in deploying the AI recruitment system effectively and ensuring compliance with the regulatory framework.",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 71409.7925000824,
        "cost_usd": 7.290000000000001e-05,
        "total_tokens": 729,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Legal Pathway and Conditions\n\n1. **Legal Basis**:\n   - The use of real-time biometric identification in a public space for law enforcement purposes is permitted under Article 5(4) of the EU AI Act, which allows for the use of such systems \"unless strictly necessary for: targeted search for victims of abduction/trafficking, prevention of imminent threat to life, or genuine terrorist threat.\"\n   - This provision intersects with GDPR Art. 9 (special categories of data \u2014 biometric data is a special category), GDPR Art. 22 (right not to be subject to solely automated decisions), and the Charter of Fundamental Rights Art. 1 (human dignity), Art. 7 (private life), Art. 8 (data protection).\n\n2. **Conditions**:\n   - The use must be strictly necessary for one of the specified purposes (targeted search for victims of abduction/trafficking, prevention of imminent threat to life, or genuine terrorist threat).\n   - The law enforcement agency must ensure that the use of biometric identification is proportionate and necessary to achieve the stated purpose.\n   - The agency must ensure that the use of biometric data is transparent and that individuals are informed about the use of their biometric data.\n   - The agency must ensure that the use of biometric data is necessary and proportionate to the threat, and that there are no other less intrusive means available to achieve the same purpose.\n\n#### Oversight Requirements\n\n1. **National Market Surveillance Authorities**:\n   - National market surveillance authorities are responsible for enforcing the prohibitions under Article 5 of the EU AI Act. They will ensure that the law enforcement agency adheres to the strict necessity requirement and other conditions.\n   - National market surveillance authorities will enforce the prohibitions, with penalties up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher, reflecting the severity of deploying banned practices.\n   - National market surveillance authorities will also enforce the prohibition on governmental social credit systems similar to China's model, and private companies using social scoring for commercial purposes may violate GDPR Art. 22 (automated decision-making) and Art. 5 (data processing principles).\n\n### Overall Confidence\nThe confidence in this synthesis is 80%. The consensus among the agents is strong, with only minor differences in specific details, which do not significantly impact the overall conclusion.\n\n### Key Evidence Sources\n1. EU AI Act, Article 5(4)\n2. GDPR, Article 9\n3. GDPR, Article 22\n4. Charter of Fundamental Rights, Articles 1, 7, 8\n5. EU AI Act, Enforcement\n6. GDPR, Enforcement\n7. National Market Surveillance Authorities, Enforcement\n\nThis synthesis is based on the consensus of the agents' outputs, with a weighted confidence of 80%.",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 100820.1938001439,
        "cost_usd": 0.0001129,
        "total_tokens": 1129,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Areas of Agreement\n1. **AI Act Art. 5 Prohibition**: Multiple agents agree that the AI system's use for risk assessment is likely to violate Art. 5(2) of the EU AI Act, which prohibits AI systems that exploit vulnerabilities of a specific group of persons, including ethnic groups.\n2. **GDPR Art. 22 (Automated Decision-Making)**: Agents agree that the AI system could be found to violate GDPR Art. 22, as it makes decisions affecting individuals, and the training data is biased, potentially leading to discriminatory outcomes.\n3. **GDPR Art. 5 (Data Processing Principles)**: Agents agree that the insurance company may be in violation of GDPR Art. 5, which requires that personal data be processed lawfully, fairly, and in a transparent manner.\n4. **GDPR Art. 9 (Special Categories of Data)**: Agents agree that the insurance company may also be in violation of GDPR Art. 9, which prohibits processing of special categories of personal data unless certain conditions are met (e.g., explicit consent, legitimate interests, or legal obligations).\n\n#### Contradictions\n1. **Agent: aiact_art5_prohibited | Type: assertion | Confidence: 50%**: The confidence level of 50% suggests that this agent's assertion is somewhat uncertain, which could lead to a more cautious approach in applying this principle.\n\n#### Synthesized Answer\nThe insurance company's use of AI for risk assessment is likely to violate multiple provisions of both the EU AI Act and the GDPR. Specifically, the AI system's use for risk assessment is likely to be found in violation of Art. 5(2) of the EU AI Act, which prohibits AI systems that exploit vulnerabilities of a specific group of persons, including ethnic groups. This violation is supported by the evidence that the training data was biased against certain ethnic groups.\n\nUnder the GDPR, the insurance company may also be in violation of Art. 22, which prohibits the use of automated decision-making in certain circumstances where the decision affects the individual. The use of biased training data for AI risk assessment could be seen as a violation of this principle. Additionally, the insurance company may be in violation of Art. 5, which requires that personal data be processed lawfully, fairly, and in a transparent manner. The use of biased training data for AI risk assessment could be seen as a violation of this principle. Furthermore, the insurance company may also be in violation of Art. 9, which prohibits processing of special categories of personal data unless certain conditions are met (e.g., explicit consent, legitimate interests, or legal obligations). Given that the training data includes sensitive characteristics (ethnicity), the insurance company may be in violation of this provision.\n\n### Overall Confidence\nThe overall confidence in the synthesized answer is 80%. The consensus among the agents supports the violation of the EU AI Act (Art. 5) and the GDPR (Art. 22, Art. 5, and Art. 9) by the insurance company's AI system. The confidence level is weighted by the confidence of the AI Act Art. 5 Prohibition agent (50%) and the agreement on the other provisions (80%).\n\n### Key Evidence Sources\n1. **EU AI Act (Art. 5)**: Evidence of biased training data against certain ethnic groups.\n2. **GDPR (Art. 22, Art. 5, Art. 9)**: Evidence of biased training data and the use of AI for risk assessment.\n\n### Final Answer\nThe insurance company's use of AI for risk assessment is likely to violate multiple provisions of both the EU AI Act and the GDPR. Specifically, the AI system's use for risk assessment is likely to be found in violation of Art. 5(2) of the EU AI Act, which prohibits AI systems that exploit vulnerabilities of a specific group of persons, including ethnic groups. This violation is supported by the evidence that the training data was biased against certain ethnic groups. Under the GDPR, the insurance company may also be in violation of Art. 22, which prohibits the use of automated decision-making in certain circumstances where the decision affects the individual. The use of biased training data for AI risk assessment could be seen as a violation of this principle. Additionally, the insurance company may be in violation of Art. 5, which requires that personal data be processed lawfully, fairly, and in a transparent manner. The use of biased training data for AI risk assessment could be seen as a violation of this principle. Furthermore, the insurance company may also be in violation of Art. 9, which prohibits processing of special categories of personal data unless certain conditions are met (e.g., explicit consent, legitimate interests, or legal obligations). Given that the training data includes sensitive characteristics (ethnicity), the insurance company may be in violation of this provision.",
        "exact_match": 0.0,
        "f1": 0.25,
        "latency_ms": 102905.80710023642,
        "cost_usd": 0.00011100000000000001,
        "total_tokens": 1110,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Areas of Agreement\n1. **Interaction with DORA's ICT Governance**: The QMS requirement of the AI Act would likely require the company to conduct regular audits and assessments of their AI systems to ensure they comply with the AI Act's prohibitions and ethical guidelines. This would intersect with DORA's ICT governance by necessitating the company to conduct regular audits and assessments to identify and mitigate any prohibited AI practices, such as those outlined in Article 5 of the AI Act.\n\n2. **Interaction with NIS2's Cybersecurity Policies**: The QMS requirement of the AI Act would require the company to ensure that their AI systems are secure and resilient against cyber threats. This would align with NIS2's cybersecurity policies, which require companies to have appropriate security measures to protect their systems and to respond to security incidents in a timely manner.\n\n#### Contradictions\n1. **Agent: aiact_art5_prohibited | Type: contradiction | Confidence: 50%**: This evidence chunk focuses on specific prohibited AI practices outlined in Article 5 of the AI Act and related enforcement details, penalties, implementation timelines, and cross-regulation implications. It does not provide information about the interaction between the AI Act's QMS requirement and DORA's ICT governance or NIS2's cybersecurity policies. Therefore, it contradicts the need to provide a synthesis of how these policies interact.\n\n### Synthesized Answer Weighted by Confidence\n\nBased on the provided evidence and logical inferences, the QMS requirement of the AI Act would intersect with DORA's ICT governance and NIS2's cybersecurity policies by requiring the company to conduct regular audits and assessments of their AI systems to ensure they comply with the AI Act's prohibitions and ethical guidelines. This would align with DORA's ICT governance by necessitating the company to conduct regular audits and assessments to identify and mitigate any prohibited AI practices. Similarly, the QMS requirement would require the company to ensure that their AI systems are secure and resilient against cyber threats, aligning with NIS2's cybersecurity policies.\n\n### Overall Confidence\nThe confidence in the synthesized answer is 75%. The evidence provided by the agents supports the synthesis, but the specific contradiction (Agent: aiact_art5_prohibited) does not provide information about the interaction between the AI Act's QMS requirement and DORA's ICT governance or NIS2's cybersecurity policies.\n\n### Key Evidence Sources\n1. Article 5 of the AI Act: Provides details on prohibited AI practices.\n2. The interaction between the AI Act's QMS requirement and DORA's ICT governance and NIS2's cybersecurity policies: Based on logical inferences and general understanding of the AI Act, DORA, and NIS2.\n3. The contradiction (Agent: aiact_art5_prohibited): Focuses on specific prohibited AI practices and their enforcement details, penalties, implementation timelines, and cross-regulation implications, which do not provide information about the interaction between the AI Act's QMS requirement and DORA's ICT governance or NIS2's cybersecurity policies.",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 100422.54470009357,
        "cost_usd": 0.00012250000000000002,
        "total_tokens": 1225,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Areas of Agreement\n- **AI Act (Article 5):** The AI Act prohibits certain AI practices, such as social scoring by public authorities or on their behalf, which could be relevant to a critical infrastructure bank context.\n- **GDPR (Article 33):** The GDPR requires organizations to report personal data breaches to the supervisory authority and affected individuals within 72 hours of becoming aware of the breach. Articles 22 (right not to be subject to solely automated decision-making) and Article 9 (special categories of data) could be relevant to AI incidents involving biometric data.\n- **NIS2 (Article 13):** The NIS2 requires organizations to report certain cybersecurity incidents to the relevant national authority within a specified timeframe.\n- **DORA (Article 12):** The DORA requires organizations to report certain incidents to the relevant national authority within a specified timeframe.\n\n#### Contradictions\n- **AI Act (Article 5):** There is a contradiction in the provided evidence as it states that Article 5(1)(c) of the AI Act prohibits social scoring by public authorities or on their behalf, which could be relevant to a critical infrastructure bank context. However, the AI Act does not provide specific incident reporting timelines, authorities, or templates.\n\n### Synthesized Answer\nBased on the provided evidence, there is no direct information available that compares and contrasts the incident reporting timelines, authorities, and templates across the AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank. The evidence focuses on specific provisions of the AI Act and related enforcement details, but does not provide information on the reporting mechanisms or templates across different regulations.\n\nHowever, we can identify areas of agreement and potential intersections between the regulations:\n\n- **AI Act (Article 5):** The AI Act prohibits certain AI practices, such as social scoring by public authorities or on their behalf, which could be relevant to a critical infrastructure bank context.\n- **GDPR (Article 33):** The GDPR requires organizations to report personal data breaches to the supervisory authority and affected individuals within 72 hours of becoming aware of the breach. Articles 22 (right not to be subject to solely automated decision-making) and Article 9 (special categories of data) could be relevant to AI incidents involving biometric data.\n- **NIS2 (Article 13):** The NIS2 requires organizations to report certain cybersecurity incidents to the relevant national authority within a specified timeframe.\n- **DORA (Article 12):** The DORA requires organizations to report certain incidents to the relevant national authority within a specified timeframe.\n\n### Overall Confidence\nThe confidence in the synthesized answer is 50%, as the provided evidence does not provide specific information on the incident reporting timelines, authorities, and templates across the AI Act, GDPR, DORA, and NIS2.\n\n### Key Evidence Sources\n- AI Act (Article 5)\n- GDPR (Article 33, Article 22, Article 9)\n- NIS2 (Article 13)\n- DORA (Article 12)\n\n### Conclusion\nThe provided evidence does not offer specific information on the incident reporting timelines, authorities, and templates across the AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank. However, we can identify areas of agreement and potential intersections between the regulations.",
        "exact_match": 0.0,
        "f1": 0.18512396694214875,
        "latency_ms": 100052.20620008186,
        "cost_usd": 0.00011200000000000001,
        "total_tokens": 1120,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "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": "### Synthesized Answer\n\n#### Areas of Agreement\n1. **Prohibition of Emotion Recognition in Workplace and Educational Institutions (Article 5(6))**: All agents agree that the use of emotion recognition systems in workplace and educational institutions is prohibited.\n2. **Cross-Regulation with GDPR Art. 9 and Art. 22**: All agents agree that the participant should also consider the intersection with GDPR, specifically Article 9(1)(e) and Article 22(1), as emotion recognition can be considered a form of automated decision-making.\n3. **EU AI Act Compliance**: All agents agree that the participant must comply with the EU AI Act, including ensuring their system does not deploy subliminal techniques, exploit vulnerabilities of specific groups, or engage in social scoring.\n\n#### Contradictions\n1. **Specificity of Article 5**: Agent `aiact_art5_prohibited` provides specific reference to Article 5(6) of the Prohibited AI Practices, while other agents do not explicitly reference this article.\n2. **Transition Period for Non-Compliance**: Agent `aiact_art5_prohibited` suggests there is no transition period for non-compliance, which is not explicitly stated by other agents.\n\n#### Synthesized Answer\nThe participant must ensure that their emotion recognition system does not engage in emotion recognition in workplace settings, does not violate GDPR provisions, and complies with the EU AI Act. They should conduct a thorough audit and decommission any non-compliant systems. This approach aligns with the legal boundaries and exceptions outlined in the evidence and the framework interactions with GDPR and the EU AI Act. The participant should also be aware of the prohibition of emotion recognition in workplace and educational institutions (Article 5(6)) and the GDPR provisions (Articles 9(1)(e) and 22(1)). Compliance with the EU AI Act, including ensuring their system does not deploy subliminal techniques, exploit vulnerabilities of specific groups, or engage in social scoring, is also crucial. There is no transition period for non-compliance, indicating a need for swift action. Confidence: 95%\n\n### Key Evidence Sources\n1. **Prohibited AI Practices**: Article 5(6) of the Prohibited AI Practices.\n2. **GDPR**: Articles 9(1)(e) and 22(1).\n3. **EU AI Act**: Article 5 of the EU AI Act.\n\nOverall Confidence: 95%",
        "exact_match": 0.0,
        "f1": 0.375,
        "latency_ms": 98644.56230029464,
        "cost_usd": 0.00011,
        "total_tokens": 1100,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      }
    ]
  }
}