{
  "$schema_version": "1.0",
  "description": "Moral orientation tags for concrete regulatory requirements, used as a descriptive analytical lens over AI instruments. Each requirement carries independent 0-3 salience on actor, action, and outcome. Pure forms are rare; most instruments mix all three. This file is hand-authored. Instrument-level rollups and ontology edges are derived by build/generate_knowledge_layer.py.",
  "srf_version": "1.0",
  "updated": "2026-07-28",
  "caveats": [
    "Pure actor, action, or outcome regimes are rare; most national and international rules are hybrids.",
    "A single provision can carry material weight on more than one dimension; salience scores are independent, not a partition that sums to 1.",
    "International soft law (notably OECD AI Principles 1.4) sits above or alongside national rules and is itself pluralist.",
    "These tags describe regulatory orientation. They do not claim that any country or agency is morally one type.",
    "Salience is an author judgment grounded in the cited text, not a measured quantity. Treat scores as ordinal, not interval."
  ],
  "dimensions": {
    "actor": {
      "id": "srf.moral.actor",
      "name": "Actor",
      "focus": "Character, duties, and competence of the people or organizations who develop, deploy, or oversee AI.",
      "typical_signals": [
        "codes of conduct",
        "named accountability",
        "due diligence on developers or providers",
        "professional or organizational obligations",
        "competence and literacy duties"
      ]
    },
    "action": {
      "id": "srf.moral.action",
      "name": "Action",
      "focus": "Rightness of the act itself, independent of realized harm (deontological).",
      "typical_signals": [
        "absolute bans",
        "mandatory process rules",
        "human-oversight gates",
        "documentation duties",
        "prohibited practices"
      ]
    },
    "outcome": {
      "id": "srf.moral.outcome",
      "name": "Outcome",
      "focus": "Foreseeable or actual impacts and harms (consequentialist).",
      "typical_signals": [
        "risk-tiering by harm potential",
        "robustness and performance thresholds",
        "continuous monitoring",
        "liability for results",
        "post-market performance duties"
      ]
    }
  },
  "salience_scale": {
    "0": "Absent from this requirement",
    "1": "Incidental: present but not the organizing logic",
    "2": "Material: a real obligation under this requirement",
    "3": "Defining: the primary moral orientation of this requirement"
  },
  "rollup_rule": "For each instrument, each dimension's rollup salience is the maximum salience among that instrument's tagged requirements. Max preserves the signal that an instrument contains a strongly action-oriented (or outcome-oriented) duty somewhere, without averaging it away.",
  "priority_instruments": [
    "eu-ai-act",
    "oecd-ai-principles",
    "nist-ai-rmf",
    "sr-26-2",
    "gdpr",
    "hipaa-security-rule",
    "uk-ai-regulation-white-paper",
    "china-genai-interim-measures",
    "canada-directive-adm",
    "cmmc-2-0",
    "sec-pda-conflicts",
    "korea-ai-basic-act",
    "australia-ai-ethics-principles"
  ],
  "requirements": [
    {
      "id": "eu-ai-act.art-5",
      "instrument": "eu-ai-act",
      "citation": "Article 5",
      "citation_match": [
        "Article 5"
      ],
      "title": "Prohibited AI practices",
      "summary": "Absolute bans on specified AI practices (for example social scoring by public authorities, certain biometric categorization, untargeted scraping for facial recognition databases).",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1",
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 3,
        "outcome": 1,
        "rationale": "The duty is not to perform the listed acts. Claimed benefits do not reopen the ban. Actor duties (who must refrain) and outcome concerns (why these acts were listed) are secondary to the prohibition itself.",
        "evidence": "Art. 5 absolute prohibitions on specific AI practices"
      }
    },
    {
      "id": "eu-ai-act.art-6",
      "instrument": "eu-ai-act",
      "citation": "Article 6",
      "citation_match": [
        "Article 6"
      ],
      "title": "Classification rules for high-risk AI systems",
      "summary": "Classifies AI systems as high-risk by reference to Annex III use cases and to AI that is a safety component of products covered by Union harmonization legislation.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 0,
        "action": 1,
        "outcome": 3,
        "rationale": "Risk class follows foreseeable harm potential of the use case or product role, not the character of the provider or a blanket ban on a technique.",
        "evidence": "Art. 6 and Annex III harm-based high-risk classification"
      }
    },
    {
      "id": "eu-ai-act.art-9",
      "instrument": "eu-ai-act",
      "citation": "Article 9",
      "citation_match": [
        "Article 9"
      ],
      "title": "Risk management system",
      "summary": "Requires a continuous iterative risk management system for high-risk AI, including identification, estimation, mitigation, and residual-risk acceptance.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1",
        "L3",
        "L4",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 2,
        "outcome": 3,
        "rationale": "The organizing logic is residual risk to health, safety, and fundamental rights. Mandatory process steps are the means; named provider duties are the carrier of those means.",
        "evidence": "Art. 9 continuous risk management keyed to foreseeable harms"
      }
    },
    {
      "id": "eu-ai-act.art-10",
      "instrument": "eu-ai-act",
      "citation": "Article 10",
      "citation_match": [
        "Article 10"
      ],
      "title": "Data and data governance",
      "summary": "Training, validation, and testing data for high-risk AI must meet quality criteria, including relevance, representativeness, and examination for bias.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L2",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 2,
        "rationale": "Provider data-governance duties (actor) and mandated examination practices (action) both serve bias and performance outcomes. No single dimension dominates.",
        "evidence": "Art. 10 data quality and bias-examination duties on providers"
      }
    },
    {
      "id": "eu-ai-act.art-14",
      "instrument": "eu-ai-act",
      "citation": "Article 14",
      "citation_match": [
        "Article 14"
      ],
      "title": "Human oversight",
      "summary": "High-risk AI must be designed so natural persons can oversee it effectively, including the ability to intervene or interrupt.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 1,
        "rationale": "The core duty is to build and operate a human-oversight gate. Competence of the overseer matters, but the requirement is structured as a mandatory process and design rule.",
        "evidence": "Art. 14 human oversight and intervene/interrupt capabilities"
      }
    },
    {
      "id": "eu-ai-act.art-15",
      "instrument": "eu-ai-act",
      "citation": "Article 15",
      "citation_match": [
        "Article 15"
      ],
      "title": "Accuracy, robustness and cybersecurity",
      "summary": "High-risk AI must achieve an appropriate level of accuracy, robustness, and cybersecurity, and be resilient against errors, faults, and unauthorized attempts to alter use or performance.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L3",
        "L4",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 1,
        "outcome": 3,
        "rationale": "Performance and resilience thresholds against foreseeable failure and attack modes are the organizing logic. Process and provider duties exist to produce those outcomes.",
        "evidence": "Art. 15 accuracy, robustness, and cybersecurity performance duties"
      }
    },
    {
      "id": "eu-ai-act.art-26",
      "instrument": "eu-ai-act",
      "citation": "Article 26",
      "citation_match": [
        "Article 26"
      ],
      "title": "Obligations of deployers of high-risk AI systems",
      "summary": "Deployers must assign human oversight, ensure input data relevance, monitor operation, and keep logs, among other duties.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1",
        "L2",
        "L3"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 2,
        "rationale": "The article assigns a bundle of duties to the deployer role. Process and monitoring requirements are substantial, but the defining move is naming who is obligated.",
        "evidence": "Art. 26 deployer obligations"
      }
    },
    {
      "id": "eu-ai-act.art-53",
      "instrument": "eu-ai-act",
      "citation": "Article 53",
      "citation_match": [
        "Article 53"
      ],
      "title": "Obligations of providers of general-purpose AI models",
      "summary": "GPAI model providers must draw up technical documentation, publish a training-data summary, and cooperate with downstream providers, with additional duties for systemic-risk models.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L5"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "Duties attach to the GPAI provider role (documentation, transparency, cooperation). Systemic-risk overlays add outcome pressure, but the article is structured as provider obligations.",
        "evidence": "Art. 53 GPAI provider obligations"
      }
    },
    {
      "id": "eu-ai-act.art-72",
      "instrument": "eu-ai-act",
      "citation": "Article 72",
      "citation_match": [
        "Article 72"
      ],
      "title": "Post-market monitoring by providers",
      "summary": "Providers of high-risk AI must establish and document a post-market monitoring system proportional to the nature of the AI technologies and the risks of the high-risk system.",
      "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689",
      "srf_layers": [
        "L1",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 3,
        "rationale": "Continuous observation of real-world performance and emerging harms is the point. Provider and process duties exist to make that observation happen.",
        "evidence": "Art. 72 post-market monitoring system"
      }
    },
    {
      "id": "oecd-ai-principles.1-4",
      "instrument": "oecd-ai-principles",
      "citation": "Principle 1.4",
      "citation_match": [
        "Principle 1.4",
        "1.4 Robustness"
      ],
      "title": "Robustness, security and safety",
      "summary": "AI systems should be robust, secure, and safe throughout their lifecycle so that in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety risk. Includes the capacity to override, repair, and decommission.",
      "url": "https://oecd.ai/en/dashboards/ai-principles/P7",
      "srf_layers": [
        "L1",
        "L3",
        "L4",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 2,
        "outcome": 3,
        "rationale": "The principle is framed around foreseeable adverse conditions and unreasonable safety risk. Override, repair, and decommission are mandated capabilities in service of that outcome. Cross-cutting soft law that most national robustness rules reuse.",
        "evidence": "OECD AI Principle 1.4 robustness, security, safety, and override/repair/decommission"
      }
    },
    {
      "id": "oecd-ai-principles.1-5",
      "instrument": "oecd-ai-principles",
      "citation": "Principle 1.5",
      "citation_match": [
        "Principle 1.5"
      ],
      "title": "Accountability",
      "summary": "AI actors should be accountable for the proper functioning of AI systems and for respect of the OECD AI Principles, based on their roles and the context.",
      "url": "https://oecd.ai/en/dashboards/ai-principles/P9",
      "srf_layers": [
        "L1",
        "L2",
        "L3",
        "L4",
        "L5"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 1,
        "outcome": 1,
        "rationale": "Accountability attaches to AI actors according to role and context. The principle names who must answer, not which acts are banned or which harm thresholds apply.",
        "evidence": "OECD AI Principle 1.5 accountability of AI actors"
      }
    },
    {
      "id": "nist-ai-rmf.govern-1-1",
      "instrument": "nist-ai-rmf",
      "citation": "GOVERN 1.1",
      "citation_match": [
        "GOVERN 1.1"
      ],
      "title": "Legal and regulatory requirements incorporated into AI risk policies",
      "summary": "Legal and regulatory requirements involving AI are understood, managed, and documented.",
      "url": "https://www.nist.gov/itl/ai-risk-management-framework",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "Organizational duty to know and manage applicable AI law. The act of incorporating requirements into policy is mandated; harm outcomes are upstream of this control.",
        "evidence": "NIST AI RMF GOVERN 1.1"
      }
    },
    {
      "id": "nist-ai-rmf.govern-2-1",
      "instrument": "nist-ai-rmf",
      "citation": "GOVERN 2.1",
      "citation_match": [
        "GOVERN 2.1"
      ],
      "title": "Roles and responsibilities for AI risk management",
      "summary": "Roles and responsibilities for AI risk and the AI risk management function are defined, assigned, and documented.",
      "url": "https://www.nist.gov/itl/ai-risk-management-framework",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 0,
        "rationale": "The requirement is to name accountable roles. Process documentation is the vehicle; outcome thresholds are not the subject.",
        "evidence": "NIST AI RMF GOVERN 2.1"
      }
    },
    {
      "id": "nist-ai-rmf.map-1-5",
      "instrument": "nist-ai-rmf",
      "citation": "MAP 1.5",
      "citation_match": [
        "MAP 1.5"
      ],
      "title": "AI system impacts on individuals, groups, and society",
      "summary": "Impacts to individuals, groups, communities, organizations, and society are characterized and documented.",
      "url": "https://www.nist.gov/itl/ai-risk-management-framework",
      "srf_layers": [
        "L1",
        "L2",
        "L3"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 1,
        "outcome": 3,
        "rationale": "Impact characterization is consequentialist by design: the requirement exists to surface foreseeable harms and benefits.",
        "evidence": "NIST AI RMF MAP 1.5"
      }
    },
    {
      "id": "nist-ai-rmf.measure-2-6",
      "instrument": "nist-ai-rmf",
      "citation": "MEASURE 2.6",
      "citation_match": [
        "MEASURE 2.6"
      ],
      "title": "AI system privacy risk measurement",
      "summary": "AI system privacy risk is examined, documented, and managed based on assessments of privacy-related impacts.",
      "url": "https://www.nist.gov/itl/ai-risk-management-framework",
      "srf_layers": [
        "L2",
        "L3",
        "L4"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 2,
        "outcome": 3,
        "rationale": "Measurement is keyed to privacy-related impacts. Mandated assessment practice supports that outcome focus.",
        "evidence": "NIST AI RMF MEASURE 2.6"
      }
    },
    {
      "id": "nist-ai-rmf.manage-2-4",
      "instrument": "nist-ai-rmf",
      "citation": "MANAGE 2.4",
      "citation_match": [
        "MANAGE 2.4"
      ],
      "title": "Risk treatment responses documented and monitored",
      "summary": "Risk treatment responses, including residual risk acceptance, are documented, monitored, and communicated.",
      "url": "https://www.nist.gov/itl/ai-risk-management-framework",
      "srf_layers": [
        "L1",
        "L3",
        "L4",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 3,
        "rationale": "Residual risk acceptance and ongoing monitoring center foreseeable impacts. Named ownership and process steps carry the outcome duty.",
        "evidence": "NIST AI RMF MANAGE 2.4"
      }
    },
    {
      "id": "sr-26-2.section-iii",
      "instrument": "sr-26-2",
      "citation": "Section III",
      "citation_match": [
        "Section III"
      ],
      "title": "Effective challenge",
      "summary": "Critical analysis by objective, informed parties that can identify model weaknesses and produce appropriate changes. Independence and competence of the challenger are central.",
      "url": "https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf",
      "srf_layers": [
        "L1",
        "L5"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "Effective challenge is defined by who challenges (informed, objective parties with stature) and the critical-analysis practice they must perform. Harm outcomes motivate the duty but do not structure the section.",
        "evidence": "SR 26-2 Section III effective challenge"
      }
    },
    {
      "id": "sr-26-2.section-iv",
      "instrument": "sr-26-2",
      "citation": "Section IV",
      "citation_match": [
        "Section IV"
      ],
      "title": "Model development, implementation, and use",
      "summary": "Expectations for sound development, including conceptual soundness, data suitability, and appropriate use relative to model purpose.",
      "url": "https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf",
      "srf_layers": [
        "L2",
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 3,
        "outcome": 2,
        "rationale": "The section mandates development and use practices. Outcome concerns (model purpose fitness) matter, but the text is organized as required process for building and deploying models.",
        "evidence": "SR 26-2 Section IV model development"
      }
    },
    {
      "id": "sr-26-2.section-v",
      "instrument": "sr-26-2",
      "citation": "Section V",
      "citation_match": [
        "Section V"
      ],
      "title": "Model validation and ongoing monitoring",
      "summary": "Independent validation and ongoing monitoring of model performance, including outcomes analysis and process verification.",
      "url": "https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf",
      "srf_layers": [
        "L1",
        "L2",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 3,
        "rationale": "Performance monitoring and outcomes analysis are the organizing logic. Independence of validators is a material actor duty supporting that outcome focus.",
        "evidence": "SR 26-2 Section V validation and ongoing monitoring"
      }
    },
    {
      "id": "sr-26-2.section-vi",
      "instrument": "sr-26-2",
      "citation": "Section VI",
      "citation_match": [
        "Section VI"
      ],
      "title": "Governance, policies, and controls",
      "summary": "Board and senior management accountability, policies, controls, and model inventory expectations for the MRM framework.",
      "url": "https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "Board and senior-management accountability and role clarity define the section. Policies and controls are the mandated acts those actors must maintain.",
        "evidence": "SR 26-2 Section VI governance and controls"
      }
    },
    {
      "id": "sr-26-2.section-vii",
      "instrument": "sr-26-2",
      "citation": "Section VII",
      "citation_match": [
        "Section VII"
      ],
      "title": "Vendor and other third-party models",
      "summary": "Institutions remain responsible for MRM of vendor and third-party models commensurate with the models' risk, including due diligence and ongoing monitoring.",
      "url": "https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf",
      "srf_layers": [
        "L1",
        "L5"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 2,
        "rationale": "Responsibility stays with the institution regardless of who built the model. Due diligence and monitoring practices serve that accountability, with risk-commensurate intensity pointing at outcomes.",
        "evidence": "SR 26-2 Section VII vendor and third-party products"
      }
    },
    {
      "id": "gdpr.art-5",
      "instrument": "gdpr",
      "citation": "Article 5",
      "citation_match": [
        "Article 5"
      ],
      "title": "Principles relating to processing of personal data",
      "summary": "Lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity/confidentiality, and accountability for personal-data processing, including AI systems that process personal data.",
      "url": "https://eur-lex.europa.eu/eli/reg/2016/679/oj",
      "srf_layers": [
        "L1",
        "L2",
        "L3"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 3,
        "outcome": 2,
        "rationale": "Accountability and controller duties name who must comply; the principles are mandatory processing acts. Harm to data subjects motivates the regime but the article is organized as required practice plus accountable actors.",
        "evidence": "GDPR Art. 5 principles and accountability"
      }
    },
    {
      "id": "gdpr.art-22",
      "instrument": "gdpr",
      "citation": "Article 22",
      "citation_match": [
        "Article 22"
      ],
      "title": "Automated individual decision-making, including profiling",
      "summary": "Gives data subjects rights against decisions based solely on automated processing that produce legal or similarly significant effects, with limited exceptions and required safeguards.",
      "url": "https://eur-lex.europa.eu/eli/reg/2016/679/oj",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 2,
        "rationale": "The duty is structured as a prohibited or constrained act (solely automated significant decisions) plus contestability safeguards. Actor rights and significant effects are material but the organizing rule is the decision practice itself.",
        "evidence": "GDPR Art. 22 automated decision-making"
      }
    },
    {
      "id": "gdpr.art-35",
      "instrument": "gdpr",
      "citation": "Article 35",
      "citation_match": [
        "Article 35"
      ],
      "title": "Data protection impact assessment",
      "summary": "Requires a DPIA where processing, especially using new technologies, is likely to result in high risk to rights and freedoms of natural persons.",
      "url": "https://eur-lex.europa.eu/eli/reg/2016/679/oj",
      "srf_layers": [
        "L1",
        "L2",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 3,
        "rationale": "High-risk to rights and freedoms is the trigger. Mandatory assessment process and controller ownership carry that outcome focus.",
        "evidence": "GDPR Art. 35 DPIA"
      }
    },
    {
      "id": "hipaa-security-rule.164-308",
      "instrument": "hipaa-security-rule",
      "citation": "45 CFR 164.308",
      "citation_match": [
        "164.308",
        "\u00a7 164.308"
      ],
      "title": "Administrative safeguards",
      "summary": "Requires covered entities and business associates to implement administrative safeguards (risk analysis, workforce security, contingency planning, evaluation) for ePHI.",
      "url": "https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-C",
      "srf_layers": [
        "L1",
        "L3",
        "L4"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 3,
        "outcome": 2,
        "rationale": "Named organizational duties and mandated safeguard practices dominate. Confidentiality, integrity, and availability of ePHI are the protected outcomes those acts serve.",
        "evidence": "HIPAA Security Rule 45 CFR 164.308"
      }
    },
    {
      "id": "hipaa-security-rule.164-312",
      "instrument": "hipaa-security-rule",
      "citation": "45 CFR 164.312",
      "citation_match": [
        "164.312",
        "\u00a7 164.312"
      ],
      "title": "Technical safeguards",
      "summary": "Requires access control, audit controls, integrity controls, and transmission security for electronic protected health information.",
      "url": "https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-C",
      "srf_layers": [
        "L3",
        "L4"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 3,
        "outcome": 2,
        "rationale": "The section mandates specific technical control practices. Outcomes (protect ePHI) motivate the rule; named actors are secondary to the required safeguards.",
        "evidence": "HIPAA Security Rule 45 CFR 164.312"
      }
    },
    {
      "id": "uk-ai-regulation-white-paper.principles",
      "instrument": "uk-ai-regulation-white-paper",
      "citation": "Five principles",
      "citation_match": [
        "five principles",
        "pro-innovation approach"
      ],
      "title": "Cross-cutting principles for UK AI regulation",
      "summary": "Sets five principles delivered through existing sector regulators rather than a single AI Act: safety/security/robustness, transparency, fairness, accountability/governance, and contestability/redress.",
      "url": "https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach",
      "srf_layers": [
        "L1",
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 2,
        "rationale": "Accountability and governance name who must act inside existing regulator remits. Safety and fairness point at outcomes; transparency and contestability are mandated process rights.",
        "evidence": "UK AI white paper five principles"
      }
    },
    {
      "id": "uk-ai-regulation-white-paper.regulator-led",
      "instrument": "uk-ai-regulation-white-paper",
      "citation": "Regulator-led delivery",
      "citation_match": [
        "sectoral regulators",
        "existing regulators"
      ],
      "title": "Sector regulators deliver AI duties",
      "summary": "Rejects a single horizontal AI statute in favor of existing sector regulators applying the five principles in context.",
      "url": "https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "The design choice is who enforces (existing sector regulators and accountable firms). Process adaptation inside remits is secondary; harm outcomes are left to sector context.",
        "evidence": "UK AI white paper regulator-led approach"
      }
    },
    {
      "id": "china-genai-interim-measures.training-data",
      "instrument": "china-genai-interim-measures",
      "citation": "Training data and model duties",
      "citation_match": [
        "training data",
        "security assessment"
      ],
      "title": "Lawful training data and security assessment",
      "summary": "Requires generative AI providers to use training data from lawful sources, respect IP and personal information rules, and complete security assessments for public-facing services.",
      "url": "https://www.cac.gov.cn/",
      "srf_layers": [
        "L1",
        "L2",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 2,
        "rationale": "Mandatory provider practices for data and assessment dominate. Provider identity matters; content and rights harms motivate the duties.",
        "evidence": "China GenAI Interim Measures training-data and assessment duties"
      }
    },
    {
      "id": "china-genai-interim-measures.content-labeling",
      "instrument": "china-genai-interim-measures",
      "citation": "Content labeling",
      "citation_match": [
        "labeling"
      ],
      "title": "Synthetic content labeling and safety",
      "summary": "Requires labeling of generated content and measures to prevent generation of illegal or harmful content by public generative AI services.",
      "url": "https://www.cac.gov.cn/",
      "srf_layers": [
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 1,
        "action": 3,
        "outcome": 3,
        "rationale": "Labeling and content-safety bans are mandated acts aimed at preventing harmful outputs. Provider role is the carrier, not the moral center.",
        "evidence": "China GenAI Interim Measures labeling and content safety"
      }
    },
    {
      "id": "canada-directive-adm.aia",
      "instrument": "canada-directive-adm",
      "citation": "Algorithmic Impact Assessment",
      "citation_match": [
        "Algorithmic Impact Assessment"
      ],
      "title": "Algorithmic Impact Assessment requirement",
      "summary": "Requires federal institutions to complete an Algorithmic Impact Assessment before production use of an automated decision system, with impact level driving peer review and transparency duties.",
      "url": "https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 2,
        "outcome": 3,
        "rationale": "Impact level and foreseeable effects on clients organize the duty. Named institutional owners and mandatory AIA process support that outcome focus.",
        "evidence": "Canada Directive on ADM Algorithmic Impact Assessment"
      }
    },
    {
      "id": "canada-directive-adm.transparency",
      "instrument": "canada-directive-adm",
      "citation": "Notice and explanations",
      "citation_match": [
        "notice that a decision",
        "meaningful explanation"
      ],
      "title": "Transparency and explanation to affected persons",
      "summary": "Requires plain-language notice that a decision will be automated and meaningful explanations of how and why the decision was made, scaled to impact level.",
      "url": "https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 2,
        "rationale": "Notice and explanation are mandated communicative acts. Institutional accountability and client impact are material supports.",
        "evidence": "Canada Directive on ADM transparency duties"
      }
    },
    {
      "id": "cmmc-2-0.access-control",
      "instrument": "cmmc-2-0",
      "citation": "Access Control domain",
      "citation_match": [
        "AC.L2",
        "AC.L3"
      ],
      "title": "Access control for CUI environments",
      "summary": "Requires limiters on system access to authorized users and processes protecting Controlled Unclassified Information in defense contractor environments.",
      "url": "https://dodcio.defense.gov/CMMC/",
      "srf_layers": [
        "L3",
        "L4"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 2,
        "rationale": "Required access-control practices are the organizing logic. Named contractor accountability and CUI confidentiality outcomes support those acts.",
        "evidence": "CMMC 2.0 Access Control practices"
      }
    },
    {
      "id": "cmmc-2-0.accountability",
      "instrument": "cmmc-2-0",
      "citation": "Audit and Accountability domain",
      "citation_match": [
        "AU.L2",
        "AU.L3"
      ],
      "title": "Audit logging and accountability",
      "summary": "Requires create, protect, and retain system audit records so actions affecting CUI systems can be traced to accountable parties.",
      "url": "https://dodcio.defense.gov/CMMC/",
      "srf_layers": [
        "L1",
        "L4"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 3,
        "outcome": 1,
        "rationale": "Traceability to accountable parties is the moral center. Mandatory logging practices implement that actor duty; mission harm is upstream context.",
        "evidence": "CMMC 2.0 Audit and Accountability"
      }
    },
    {
      "id": "sec-pda-conflicts.investor-interest",
      "instrument": "sec-pda-conflicts",
      "citation": "Investor-interest conflicts",
      "citation_match": [
        "predictive data analytics"
      ],
      "title": "Firm vs investor conflicts in PDA/AI tools",
      "summary": "Proposed SEC approach would require firms to eliminate or neutralize conflicts when predictive analytics or similar AI optimizations place firm interests ahead of investor interests.",
      "url": "https://www.sec.gov/rules-regulations/proposed-rules",
      "srf_layers": [
        "L1",
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 3,
        "rationale": "The proposed duty targets harmful conflict outcomes for investors and mandates elimination or neutralization practices. Firm accountability is the carrier.",
        "evidence": "SEC PDA conflicts proposed rule investor-interest framing"
      }
    },
    {
      "id": "korea-ai-basic-act.governance",
      "instrument": "korea-ai-basic-act",
      "citation": "National AI governance",
      "citation_match": [
        "AI Basic Act"
      ],
      "title": "National AI governance structure",
      "summary": "Establishes national AI governance bodies and policy coordination duties for the Korean government and covered AI businesses.",
      "url": "https://www.msit.go.kr/",
      "srf_layers": [
        "L1"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 1,
        "rationale": "Named national and organizational accountability is the primary orientation. Process and risk duties follow from that structure.",
        "evidence": "Korea AI Basic Act governance provisions"
      }
    },
    {
      "id": "korea-ai-basic-act.transparency",
      "instrument": "korea-ai-basic-act",
      "citation": "Transparency and safety duties",
      "citation_match": [
        "AI Basic Act"
      ],
      "title": "Transparency and safety obligations",
      "summary": "Imposes transparency and safety-related duties on AI businesses, with higher expectations where systems can significantly affect rights or safety.",
      "url": "https://www.msit.go.kr/",
      "srf_layers": [
        "L1",
        "L3",
        "L5"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 3,
        "rationale": "Mandated transparency and safety practices aim at preventing significant rights and safety harms. Business accountability is material but secondary to the required acts and outcomes.",
        "evidence": "Korea AI Basic Act transparency and safety duties"
      }
    },
    {
      "id": "australia-ai-ethics-principles.accountability",
      "instrument": "australia-ai-ethics-principles",
      "citation": "Accountability",
      "citation_match": [
        "Australia's AI Ethics Principles"
      ],
      "title": "Accountability principle",
      "summary": "Those responsible for AI systems should be identifiable and accountable for the outcomes of the AI systems, with appropriate human oversight.",
      "url": "https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 3,
        "action": 2,
        "outcome": 2,
        "rationale": "Identifiable responsible parties define the principle. Oversight practices and outcome ownership are supporting duties.",
        "evidence": "Australia AI Ethics Principles accountability"
      }
    },
    {
      "id": "australia-ai-ethics-principles.contestability",
      "instrument": "australia-ai-ethics-principles",
      "citation": "Contestability",
      "citation_match": [
        "contestability"
      ],
      "title": "Contestability principle",
      "summary": "When an AI system significantly affects a person, there should be timely processes to allow people to challenge use or outcomes of the AI system.",
      "url": "https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles",
      "srf_layers": [
        "L1",
        "L3"
      ],
      "moral_profile": {
        "actor": 2,
        "action": 3,
        "outcome": 2,
        "rationale": "The principle mandates contestation processes for significant AI effects. Affected persons and harm stakes matter; the organizing requirement is the challenge practice.",
        "evidence": "Australia AI Ethics Principles contestability"
      }
    }
  ],
  "citation_match_note": "citation_match patterns are tested only against citations filed under the mapping_key of this requirement's own instrument. Cross-instrument text collisions therefore cannot create an implements edge, so patterns can stay readable. Patterns must match the citation text a control actually writes: an instrument name alone will not match a schema that cites clause IDs. Requirements listed in unmatched_expected are known to have no implementing control; the verifier fails on any other requirement whose instrument is cited but whose patterns match nothing, and on stale entries that have since matched.",
  "unmatched_expected": {
    "eu-ai-act.art-5": "Prohibited practices are a ban on deployment, not a duty a control implements, so no schema cites Article 5.",
    "eu-ai-act.art-6": "Article 6 sets high-risk classification scope rather than imposing an operational duty.",
    "gdpr.art-5": "GDPR is join-ready but no vertical schema cites it yet.",
    "gdpr.art-22": "GDPR is join-ready but no vertical schema cites it yet.",
    "gdpr.art-35": "GDPR is join-ready but no vertical schema cites it yet."
  }
}