ai-governance

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AI Governance

AI治理

Comprehensive guidance for AI governance, regulatory compliance, and responsible AI practices, including EU AI Act and NIST AI Risk Management Framework.
本指南全面介绍AI治理、法规合规及负责任AI实践,涵盖欧盟AI法案与NIST AI风险管理框架。

When to Use This Skill

何时使用本技能

  • Classifying AI systems under EU AI Act risk categories
  • Conducting AI risk assessments using NIST AI RMF
  • Implementing responsible AI principles
  • Preparing for AI compliance audits
  • Creating AI system documentation and model cards
  • Establishing AI governance frameworks
  • Conducting AI ethics reviews
  • 依据欧盟AI法案对AI系统进行风险分类
  • 使用NIST AI RMF开展AI风险评估
  • 落实负责任AI原则
  • 为AI合规审计做准备
  • 编写AI系统文档与模型卡片
  • 建立AI治理框架
  • 开展AI伦理审查

Quick Reference

快速参考

EU AI Act Risk Classification

欧盟AI法案风险分类

Risk LevelDescriptionExamplesRequirements
UnacceptableProhibited practicesSocial scoring, subliminal manipulation, exploitation of vulnerabilitiesBanned outright
High-RiskSafety/rights impactEmployment AI, credit scoring, biometric ID, critical infrastructureStrict compliance
Limited RiskTransparency neededChatbots, emotion recognition, deepfakesDisclosure required
Minimal RiskLow/no regulationSpam filters, game AI, recommendation systemsVoluntary codes
风险等级描述示例要求
不可接受被禁止的实践社会评分、潜意识操控、利用弱势群体漏洞完全禁止
高风险对安全/权利有影响就业AI、信用评分、生物识别ID、关键基础设施严格合规
有限风险需要透明度聊天机器人、情绪识别、深度伪造需披露信息
最小风险低/无监管垃圾邮件过滤器、游戏AI、推荐系统自愿遵循准则

NIST AI RMF Functions

NIST AI RMF功能

FunctionPurposeKey Activities
GovernCultivate risk culturePolicies, accountability, governance structures
MapUnderstand contextStakeholders, impacts, constraints, requirements
MeasureAssess and trackRisk metrics, testing, monitoring, evaluation
ManagePrioritize and actMitigations, responses, documentation
功能目标核心活动
治理培育风险文化政策制定、问责机制、治理结构
映射理解应用场景利益相关方、影响、约束条件、需求
度量评估与跟踪风险风险指标、测试、监控、评估
管理优先处理并采取行动风险缓解、响应措施、文档记录

Responsible AI Principles

负责任AI原则

PrincipleDescriptionImplementation
FairnessEquitable treatment, bias mitigationFairness metrics, bias testing, diverse data
TransparencyExplainable decisionsXAI methods, model cards, documentation
AccountabilityClear ownership and oversightGovernance roles, audit trails, escalation
PrivacyData protection, consentPII handling, anonymization, consent management
SafetyReliable, secure operationTesting, monitoring, incident response
Human OversightMeaningful human controlHITL design, override mechanisms, review
原则描述实施方式
公平性公平对待,缓解偏见公平性指标、偏见测试、多样化数据
透明度决策可解释XAI方法、模型卡片、文档记录
问责制明确所有权与监督治理角色、审计追踪、升级流程
隐私保护数据保护、知情同意PII处理、匿名化、同意管理
安全性可靠、安全运行测试、监控、事件响应
人工监督有意义的人工控制人在回路设计、覆盖机制、审查流程

EU AI Act Compliance

欧盟AI法案合规要求

Prohibited AI Practices (Article 5)

被禁止的AI实践(第5条)

yaml
prohibited_practices:
  social_scoring:
    description: "General-purpose social credit systems"
    applies_to: "Public authorities scoring citizens"
    prohibition: "Absolute - no exceptions"

  subliminal_manipulation:
    description: "AI exploiting subconscious to cause harm"
    applies_to: "Systems using techniques beyond awareness"
    prohibition: "Absolute - no exceptions"

  vulnerability_exploitation:
    description: "AI exploiting age, disability, social situation"
    applies_to: "Systems targeting vulnerable groups"
    prohibition: "Absolute - no exceptions"

  real_time_biometric_identification:
    description: "Remote biometric ID in public spaces"
    applies_to: "Law enforcement use"
    exceptions:
      - "Search for missing children"
      - "Prevention of terrorist attack"
      - "Identification of criminal suspects"
    authorization: "Prior judicial or administrative approval required"

  emotion_inference_workplace:
    description: "Emotion recognition in workplace/education"
    applies_to: "Employee/student monitoring"
    exceptions:
      - "Medical or safety purposes"

  predictive_policing:
    description: "Individual crime risk based solely on profiling"
    applies_to: "Law enforcement prediction"
    prohibition: "Absolute when based solely on profiling/traits"

  facial_recognition_scraping:
    description: "Untargeted facial image collection"
    applies_to: "Databases built from internet/CCTV scraping"
    prohibition: "Absolute - no exceptions"
yaml
prohibited_practices:
  social_scoring:
    description: "General-purpose social credit systems"
    applies_to: "Public authorities scoring citizens"
    prohibition: "Absolute - no exceptions"

  subliminal_manipulation:
    description: "AI exploiting subconscious to cause harm"
    applies_to: "Systems using techniques beyond awareness"
    prohibition: "Absolute - no exceptions"

  vulnerability_exploitation:
    description: "AI exploiting age, disability, social situation"
    applies_to: "Systems targeting vulnerable groups"
    prohibition: "Absolute - no exceptions"

  real_time_biometric_identification:
    description: "Remote biometric ID in public spaces"
    applies_to: "Law enforcement use"
    exceptions:
      - "Search for missing children"
      - "Prevention of terrorist attack"
      - "Identification of criminal suspects"
    authorization: "Prior judicial or administrative approval required"

  emotion_inference_workplace:
    description: "Emotion recognition in workplace/education"
    applies_to: "Employee/student monitoring"
    exceptions:
      - "Medical or safety purposes"

  predictive_policing:
    description: "Individual crime risk based solely on profiling"
    applies_to: "Law enforcement prediction"
    prohibition: "Absolute when based solely on profiling/traits"

  facial_recognition_scraping:
    description: "Untargeted facial image collection"
    applies_to: "Databases built from internet/CCTV scraping"
    prohibition: "Absolute - no exceptions"

High-Risk AI Classification (Annex III)

高风险AI分类(附件III)

yaml
high_risk_categories:
  biometrics:
    - "Remote biometric identification systems"
    - "Biometric categorization (race, political, religion)"
    - "Emotion recognition systems"

  critical_infrastructure:
    - "Safety components in road traffic"
    - "Water, gas, heating, electricity management"
    - "Digital infrastructure safety components"

  education_training:
    - "Educational/vocational access decisions"
    - "Exam evaluation (learning outcomes)"
    - "Behavior assessment in institutions"

  employment:
    - "Recruitment and candidate filtering"
    - "Job advertisement targeting"
    - "Application evaluation"
    - "Promotion/termination decisions"
    - "Task allocation based on behavior/traits"
    - "Performance monitoring"

  essential_services:
    - "Credit scoring and creditworthiness"
    - "Risk assessment in life/health insurance"
    - "Emergency services dispatch prioritization"

  law_enforcement:
    - "Individual risk assessment (re-offending)"
    - "Polygraph and similar tools"
    - "Evidence reliability assessment"
    - "Crime prediction for individuals/groups"
    - "Profiling during investigations"

  migration_asylum:
    - "Polygraphs and similar at borders"
    - "Risk assessment (security, health, irregular entry)"
    - "Verification of travel document authenticity"
    - "Asylum/visa/residence application processing"

  justice_democracy:
    - "AI assisting judicial research/interpretation"
    - "AI assisting application of law to facts"
    - "Alternative dispute resolution"
    - "Election/referendum influence"
yaml
high_risk_categories:
  biometrics:
    - "Remote biometric identification systems"
    - "Biometric categorization (race, political, religion)"
    - "Emotion recognition systems"

  critical_infrastructure:
    - "Safety components in road traffic"
    - "Water, gas, heating, electricity management"
    - "Digital infrastructure safety components"

  education_training:
    - "Educational/vocational access decisions"
    - "Exam evaluation (learning outcomes)"
    - "Behavior assessment in institutions"

  employment:
    - "Recruitment and candidate filtering"
    - "Job advertisement targeting"
    - "Application evaluation"
    - "Promotion/termination decisions"
    - "Task allocation based on behavior/traits"
    - "Performance monitoring"

  essential_services:
    - "Credit scoring and creditworthiness"
    - "Risk assessment in life/health insurance"
    - "Emergency services dispatch prioritization"

  law_enforcement:
    - "Individual risk assessment (re-offending)"
    - "Polygraph and similar tools"
    - "Evidence reliability assessment"
    - "Crime prediction for individuals/groups"
    - "Profiling during investigations"

  migration_asylum:
    - "Polygraphs and similar at borders"
    - "Risk assessment (security, health, irregular entry)"
    - "Verification of travel document authenticity"
    - "Asylum/visa/residence application processing"

  justice_democracy:
    - "AI assisting judicial research/interpretation"
    - "AI assisting application of law to facts"
    - "Alternative dispute resolution"
    - "Election/referendum influence"

High-Risk AI Requirements

高风险AI要求

csharp
namespace Security.AIGovernance;

/// <summary>
/// EU AI Act high-risk AI system requirements.
/// </summary>
public static class HighRiskRequirements
{
    /// <summary>
    /// Risk management system requirements (Article 9).
    /// </summary>
    public static readonly RiskManagementRequirements RiskManagement = new(
        ContinuousProcess: true,
        IdentifyKnownRisks: true,
        EstimateRiskLevels: true,
        EvaluateEmergingRisks: true,
        AdoptMitigations: true,
        DocumentDecisions: true,
        TestingRequirements: [
            "Testing against defined metrics",
            "Testing with representative data",
            "Testing for foreseeable misuse",
            "Testing by independent parties where appropriate"
        ]
    );

    /// <summary>
    /// Data and data governance requirements (Article 10).
    /// </summary>
    public static readonly DataGovernanceRequirements DataGovernance = new(
        TrainingDataDocumentation: true,
        DataQualityManagement: true,
        BiasExamination: true,
        RelevanceVerification: true,
        RepresentativenessCheck: true,
        SpecialCategoryDataHandling: [
            "Strictly necessary for bias detection",
            "Subject to appropriate safeguards",
            "Not used for other purposes"
        ]
    );

    /// <summary>
    /// Technical documentation requirements (Article 11).
    /// </summary>
    public static readonly TechnicalDocumentationRequirements Documentation = new(
        GeneralDescription: true,
        IntendedPurpose: true,
        DesignSpecifications: true,
        SystemArchitecture: true,
        DataRequirements: true,
        TrainingMethodologies: true,
        ValidationProcedures: true,
        PerformanceMetrics: true,
        RiskManagementSystem: true,
        Cybersecurity: true,
        ModificationLog: true
    );

    /// <summary>
    /// Record-keeping requirements (Article 12).
    /// </summary>
    public static readonly RecordKeepingRequirements RecordKeeping = new(
        AutomaticLogging: true,
        OperationalLogs: true,
        IdentityOfUsers: true,
        DateTimeOfUse: true,
        ReferenceInputData: true,
        OutputData: true,
        RetentionPeriod: "Appropriate to intended purpose"
    );

    /// <summary>
    /// Transparency requirements (Article 13).
    /// </summary>
    public static readonly TransparencyRequirements Transparency = new(
        ClearInstructions: true,
        ProviderIdentity: true,
        SystemCapabilities: true,
        SystemLimitations: true,
        AccuracyLevels: true,
        ForeseeableRisks: true,
        HumanOversightMeasures: true,
        MaintenanceRequirements: true
    );

    /// <summary>
    /// Human oversight requirements (Article 14).
    /// </summary>
    public static readonly HumanOversightRequirements HumanOversight = new(
        DesignedForOversight: true,
        OperatorTools: [
            "Understand system capabilities and limitations",
            "Monitor operation correctly",
            "Detect automation bias",
            "Interpret outputs correctly",
            "Override or interrupt system",
            "Decide not to use or disregard output"
        ],
        Proportionate: "To risks and autonomy level"
    );
}

public sealed record RiskManagementRequirements(
    bool ContinuousProcess,
    bool IdentifyKnownRisks,
    bool EstimateRiskLevels,
    bool EvaluateEmergingRisks,
    bool AdoptMitigations,
    bool DocumentDecisions,
    string[] TestingRequirements);

public sealed record DataGovernanceRequirements(
    bool TrainingDataDocumentation,
    bool DataQualityManagement,
    bool BiasExamination,
    bool RelevanceVerification,
    bool RepresentativenessCheck,
    string[] SpecialCategoryDataHandling);

public sealed record TechnicalDocumentationRequirements(
    bool GeneralDescription,
    bool IntendedPurpose,
    bool DesignSpecifications,
    bool SystemArchitecture,
    bool DataRequirements,
    bool TrainingMethodologies,
    bool ValidationProcedures,
    bool PerformanceMetrics,
    bool RiskManagementSystem,
    bool Cybersecurity,
    bool ModificationLog);

public sealed record RecordKeepingRequirements(
    bool AutomaticLogging,
    bool OperationalLogs,
    bool IdentityOfUsers,
    bool DateTimeOfUse,
    bool ReferenceInputData,
    bool OutputData,
    string RetentionPeriod);

public sealed record TransparencyRequirements(
    bool ClearInstructions,
    bool ProviderIdentity,
    bool SystemCapabilities,
    bool SystemLimitations,
    bool AccuracyLevels,
    bool ForeseeableRisks,
    bool HumanOversightMeasures,
    bool MaintenanceRequirements);

public sealed record HumanOversightRequirements(
    bool DesignedForOversight,
    string[] OperatorTools,
    string Proportionate);
csharp
namespace Security.AIGovernance;

/// <summary>
/// EU AI Act high-risk AI system requirements.
/// </summary>
public static class HighRiskRequirements
{
    /// <summary>
    /// Risk management system requirements (Article 9).
    /// </summary>
    public static readonly RiskManagementRequirements RiskManagement = new(
        ContinuousProcess: true,
        IdentifyKnownRisks: true,
        EstimateRiskLevels: true,
        EvaluateEmergingRisks: true,
        AdoptMitigations: true,
        DocumentDecisions: true,
        TestingRequirements: [
            "Testing against defined metrics",
            "Testing with representative data",
            "Testing for foreseeable misuse",
            "Testing by independent parties where appropriate"
        ]
    );

    /// <summary>
    /// Data and data governance requirements (Article 10).
    /// </summary>
    public static readonly DataGovernanceRequirements DataGovernance = new(
        TrainingDataDocumentation: true,
        DataQualityManagement: true,
        BiasExamination: true,
        RelevanceVerification: true,
        RepresentativenessCheck: true,
        SpecialCategoryDataHandling: [
            "Strictly necessary for bias detection",
            "Subject to appropriate safeguards",
            "Not used for other purposes"
        ]
    );

    /// <summary>
    /// Technical documentation requirements (Article 11).
    /// </summary>
    public static readonly TechnicalDocumentationRequirements Documentation = new(
        GeneralDescription: true,
        IntendedPurpose: true,
        DesignSpecifications: true,
        SystemArchitecture: true,
        DataRequirements: true,
        TrainingMethodologies: true,
        ValidationProcedures: true,
        PerformanceMetrics: true,
        RiskManagementSystem: true,
        Cybersecurity: true,
        ModificationLog: true
    );

    /// <summary>
    /// Record-keeping requirements (Article 12).
    /// </summary>
    public static readonly RecordKeepingRequirements RecordKeeping = new(
        AutomaticLogging: true,
        OperationalLogs: true,
        IdentityOfUsers: true,
        DateTimeOfUse: true,
        ReferenceInputData: true,
        OutputData: true,
        RetentionPeriod: "Appropriate to intended purpose"
    );

    /// <summary>
    /// Transparency requirements (Article 13).
    /// </summary>
    public static readonly TransparencyRequirements Transparency = new(
        ClearInstructions: true,
        ProviderIdentity: true,
        SystemCapabilities: true,
        SystemLimitations: true,
        AccuracyLevels: true,
        ForeseeableRisks: true,
        HumanOversightMeasures: true,
        MaintenanceRequirements: true
    );

    /// <summary>
    /// Human oversight requirements (Article 14).
    /// </summary>
    public static readonly HumanOversightRequirements HumanOversight = new(
        DesignedForOversight: true,
        OperatorTools: [
            "Understand system capabilities and limitations",
            "Monitor operation correctly",
            "Detect automation bias",
            "Interpret outputs correctly",
            "Override or interrupt system",
            "Decide not to use or disregard output"
        ],
        Proportionate: "To risks and autonomy level"
    );
}

public sealed record RiskManagementRequirements(
    bool ContinuousProcess,
    bool IdentifyKnownRisks,
    bool EstimateRiskLevels,
    bool EvaluateEmergingRisks,
    bool AdoptMitigations,
    bool DocumentDecisions,
    string[] TestingRequirements);

public sealed record DataGovernanceRequirements(
    bool TrainingDataDocumentation,
    bool DataQualityManagement,
    bool BiasExamination,
    bool RelevanceVerification,
    bool RepresentativenessCheck,
    string[] SpecialCategoryDataHandling);

public sealed record TechnicalDocumentationRequirements(
    bool GeneralDescription,
    bool IntendedPurpose,
    bool DesignSpecifications,
    bool SystemArchitecture,
    bool DataRequirements,
    bool TrainingMethodologies,
    bool ValidationProcedures,
    bool PerformanceMetrics,
    bool RiskManagementSystem,
    bool Cybersecurity,
    bool ModificationLog);

public sealed record RecordKeepingRequirements(
    bool AutomaticLogging,
    bool OperationalLogs,
    bool IdentityOfUsers,
    bool DateTimeOfUse,
    bool ReferenceInputData,
    bool OutputData,
    string RetentionPeriod);

public sealed record TransparencyRequirements(
    bool ClearInstructions,
    bool ProviderIdentity,
    bool SystemCapabilities,
    bool SystemLimitations,
    bool AccuracyLevels,
    bool ForeseeableRisks,
    bool HumanOversightMeasures,
    bool MaintenanceRequirements);

public sealed record HumanOversightRequirements(
    bool DesignedForOversight,
    string[] OperatorTools,
    string Proportionate);

NIST AI Risk Management Framework

NIST AI风险管理框架

Govern Function

治理功能

yaml
govern_function:
  description: "Cultivate a culture of risk management"

  govern_1:
    name: "Policies and Procedures"
    activities:
      - "Establish AI governance policies"
      - "Define AI risk tolerances"
      - "Create AI development standards"
      - "Document ethical guidelines"
    outputs:
      - "AI governance policy"
      - "Risk appetite statement"
      - "Development standards"

  govern_2:
    name: "Accountability Structures"
    activities:
      - "Define AI ownership roles"
      - "Establish oversight committees"
      - "Create escalation paths"
      - "Assign compliance responsibilities"
    outputs:
      - "RACI matrix for AI systems"
      - "Governance org chart"
      - "Escalation procedures"

  govern_3:
    name: "Workforce Diversity"
    activities:
      - "Diverse team composition"
      - "Inclusive development practices"
      - "Bias awareness training"
      - "Cross-functional collaboration"
    outputs:
      - "Diversity metrics"
      - "Training records"
      - "Team composition reports"

  govern_4:
    name: "Organizational Culture"
    activities:
      - "Promote responsible AI values"
      - "Encourage ethical considerations"
      - "Support risk identification"
      - "Foster transparency"
    outputs:
      - "Culture assessment results"
      - "Ethics training completion"
      - "Feedback mechanisms"

  govern_5:
    name: "Stakeholder Engagement"
    activities:
      - "Identify affected stakeholders"
      - "Establish feedback channels"
      - "Incorporate stakeholder input"
      - "Communicate AI decisions"
    outputs:
      - "Stakeholder registry"
      - "Engagement records"
      - "Communication plan"

  govern_6:
    name: "Legal Compliance"
    activities:
      - "Map regulatory requirements"
      - "Monitor regulatory changes"
      - "Ensure compliance verification"
      - "Maintain audit readiness"
    outputs:
      - "Compliance matrix"
      - "Regulatory tracker"
      - "Audit schedules"
yaml
govern_function:
  description: "Cultivate a culture of risk management"

  govern_1:
    name: "Policies and Procedures"
    activities:
      - "Establish AI governance policies"
      - "Define AI risk tolerances"
      - "Create AI development standards"
      - "Document ethical guidelines"
    outputs:
      - "AI governance policy"
      - "Risk appetite statement"
      - "Development standards"

  govern_2:
    name: "Accountability Structures"
    activities:
      - "Define AI ownership roles"
      - "Establish oversight committees"
      - "Create escalation paths"
      - "Assign compliance responsibilities"
    outputs:
      - "RACI matrix for AI systems"
      - "Governance org chart"
      - "Escalation procedures"

  govern_3:
    name: "Workforce Diversity"
    activities:
      - "Diverse team composition"
      - "Inclusive development practices"
      - "Bias awareness training"
      - "Cross-functional collaboration"
    outputs:
      - "Diversity metrics"
      - "Training records"
      - "Team composition reports"

  govern_4:
    name: "Organizational Culture"
    activities:
      - "Promote responsible AI values"
      - "Encourage ethical considerations"
      - "Support risk identification"
      - "Foster transparency"
    outputs:
      - "Culture assessment results"
      - "Ethics training completion"
      - "Feedback mechanisms"

  govern_5:
    name: "Stakeholder Engagement"
    activities:
      - "Identify affected stakeholders"
      - "Establish feedback channels"
      - "Incorporate stakeholder input"
      - "Communicate AI decisions"
    outputs:
      - "Stakeholder registry"
      - "Engagement records"
      - "Communication plan"

  govern_6:
    name: "Legal Compliance"
    activities:
      - "Map regulatory requirements"
      - "Monitor regulatory changes"
      - "Ensure compliance verification"
      - "Maintain audit readiness"
    outputs:
      - "Compliance matrix"
      - "Regulatory tracker"
      - "Audit schedules"

Map Function

映射功能

yaml
map_function:
  description: "Understand the context and impacts"

  map_1:
    name: "Intended Purpose"
    activities:
      - "Document business objectives"
      - "Define use case boundaries"
      - "Identify target users"
      - "Specify deployment context"
    outputs:
      - "Use case specification"
      - "User personas"
      - "Deployment plan"

  map_2:
    name: "Categorization"
    activities:
      - "Classify AI system type"
      - "Determine risk category"
      - "Identify regulatory applicability"
      - "Assess criticality level"
    outputs:
      - "Risk classification"
      - "Regulatory mapping"
      - "Criticality assessment"

  map_3:
    name: "Impacts and Affected Parties"
    activities:
      - "Identify potential harms"
      - "Map affected populations"
      - "Assess differential impacts"
      - "Consider cumulative effects"
    outputs:
      - "Impact assessment"
      - "Affected party analysis"
      - "Equity considerations"

  map_4:
    name: "Dependencies"
    activities:
      - "Document data sources"
      - "Identify third-party components"
      - "Map system integrations"
      - "Assess supply chain risks"
    outputs:
      - "Dependency inventory"
      - "Third-party risk assessment"
      - "Integration diagram"

  map_5:
    name: "Risk Identification"
    activities:
      - "Enumerate potential risks"
      - "Consider failure modes"
      - "Assess adversarial threats"
      - "Evaluate misuse potential"
    outputs:
      - "Risk register"
      - "Threat model"
      - "Misuse scenarios"
yaml
map_function:
  description: "Understand the context and impacts"

  map_1:
    name: "Intended Purpose"
    activities:
      - "Document business objectives"
      - "Define use case boundaries"
      - "Identify target users"
      - "Specify deployment context"
    outputs:
      - "Use case specification"
      - "User personas"
      - "Deployment plan"

  map_2:
    name: "Categorization"
    activities:
      - "Classify AI system type"
      - "Determine risk category"
      - "Identify regulatory applicability"
      - "Assess criticality level"
    outputs:
      - "Risk classification"
      - "Regulatory mapping"
      - "Criticality assessment"

  map_3:
    name: "Impacts and Affected Parties"
    activities:
      - "Identify potential harms"
      - "Map affected populations"
      - "Assess differential impacts"
      - "Consider cumulative effects"
    outputs:
      - "Impact assessment"
      - "Affected party analysis"
      - "Equity considerations"

  map_4:
    name: "Dependencies"
    activities:
      - "Document data sources"
      - "Identify third-party components"
      - "Map system integrations"
      - "Assess supply chain risks"
    outputs:
      - "Dependency inventory"
      - "Third-party risk assessment"
      - "Integration diagram"

  map_5:
    name: "Risk Identification"
    activities:
      - "Enumerate potential risks"
      - "Consider failure modes"
      - "Assess adversarial threats"
      - "Evaluate misuse potential"
    outputs:
      - "Risk register"
      - "Threat model"
      - "Misuse scenarios"

Measure Function

度量功能

yaml
measure_function:
  description: "Assess and track risks"

  measure_1:
    name: "Risk Metrics"
    activities:
      - "Define risk indicators"
      - "Establish measurement methods"
      - "Set thresholds and tolerances"
      - "Create monitoring dashboards"
    outputs:
      - "KRI definitions"
      - "Measurement protocols"
      - "Threshold documentation"

  measure_2:
    name: "Testing and Evaluation"
    activities:
      - "Conduct bias testing"
      - "Evaluate model performance"
      - "Test edge cases"
      - "Assess robustness"
    outputs:
      - "Test results"
      - "Performance metrics"
      - "Robustness report"

  measure_3:
    name: "Continuous Monitoring"
    activities:
      - "Monitor model drift"
      - "Track performance degradation"
      - "Detect anomalies"
      - "Log incidents"
    outputs:
      - "Monitoring reports"
      - "Drift analysis"
      - "Incident logs"

  measure_4:
    name: "Independent Assessment"
    activities:
      - "Conduct internal audits"
      - "Engage external reviewers"
      - "Facilitate red teaming"
      - "Perform algorithmic audits"
    outputs:
      - "Audit reports"
      - "External review findings"
      - "Red team results"
yaml
measure_function:
  description: "Assess and track risks"

  measure_1:
    name: "Risk Metrics"
    activities:
      - "Define risk indicators"
      - "Establish measurement methods"
      - "Set thresholds and tolerances"
      - "Create monitoring dashboards"
    outputs:
      - "KRI definitions"
      - "Measurement protocols"
      - "Threshold documentation"

  measure_2:
    name: "Testing and Evaluation"
    activities:
      - "Conduct bias testing"
      - "Evaluate model performance"
      - "Test edge cases"
      - "Assess robustness"
    outputs:
      - "Test results"
      - "Performance metrics"
      - "Robustness report"

  measure_3:
    name: "Continuous Monitoring"
    activities:
      - "Monitor model drift"
      - "Track performance degradation"
      - "Detect anomalies"
      - "Log incidents"
    outputs:
      - "Monitoring reports"
      - "Drift analysis"
      - "Incident logs"

  measure_4:
    name: "Independent Assessment"
    activities:
      - "Conduct internal audits"
      - "Engage external reviewers"
      - "Facilitate red teaming"
      - "Perform algorithmic audits"
    outputs:
      - "Audit reports"
      - "External review findings"
      - "Red team results"

Manage Function

管理功能

yaml
manage_function:
  description: "Prioritize and respond to risks"

  manage_1:
    name: "Risk Prioritization"
    activities:
      - "Rank risks by severity"
      - "Assess likelihood and impact"
      - "Prioritize mitigation efforts"
      - "Allocate resources"
    outputs:
      - "Prioritized risk register"
      - "Resource allocation plan"
      - "Mitigation roadmap"

  manage_2:
    name: "Risk Response"
    activities:
      - "Implement mitigations"
      - "Develop contingency plans"
      - "Create rollback procedures"
      - "Document decisions"
    outputs:
      - "Mitigation implementations"
      - "Contingency plans"
      - "Rollback procedures"

  manage_3:
    name: "Residual Risk"
    activities:
      - "Assess remaining risks"
      - "Obtain risk acceptance"
      - "Document limitations"
      - "Communicate constraints"
    outputs:
      - "Residual risk assessment"
      - "Risk acceptance records"
      - "Limitation documentation"

  manage_4:
    name: "Documentation and Communication"
    activities:
      - "Maintain risk documentation"
      - "Report to stakeholders"
      - "Share lessons learned"
      - "Update governance artifacts"
    outputs:
      - "Risk documentation"
      - "Stakeholder reports"
      - "Lessons learned"
yaml
manage_function:
  description: "Prioritize and respond to risks"

  manage_1:
    name: "Risk Prioritization"
    activities:
      - "Rank risks by severity"
      - "Assess likelihood and impact"
      - "Prioritize mitigation efforts"
      - "Allocate resources"
    outputs:
      - "Prioritized risk register"
      - "Resource allocation plan"
      - "Mitigation roadmap"

  manage_2:
    name: "Risk Response"
    activities:
      - "Implement mitigations"
      - "Develop contingency plans"
      - "Create rollback procedures"
      - "Document decisions"
    outputs:
      - "Mitigation implementations"
      - "Contingency plans"
      - "Rollback procedures"

  manage_3:
    name: "Residual Risk"
    activities:
      - "Assess remaining risks"
      - "Obtain risk acceptance"
      - "Document limitations"
      - "Communicate constraints"
    outputs:
      - "Residual risk assessment"
      - "Risk acceptance records"
      - "Limitation documentation"

  manage_4:
    name: "Documentation and Communication"
    activities:
      - "Maintain risk documentation"
      - "Report to stakeholders"
      - "Share lessons learned"
      - "Update governance artifacts"
    outputs:
      - "Risk documentation"
      - "Stakeholder reports"
      - "Lessons learned"

AI Risk Assessment

AI风险评估

Risk Classification Model

风险分类模型

csharp
namespace Security.AIGovernance;

/// <summary>
/// AI system risk classification and assessment.
/// </summary>
public sealed class AIRiskAssessment
{
    /// <summary>
    /// Classify AI system risk level based on characteristics.
    /// </summary>
    public static RiskClassification ClassifyRisk(AISystemCharacteristics system)
    {
        // Check for prohibited practices first
        if (IsProhibited(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Unacceptable,
                Reasoning: "System falls under EU AI Act prohibited practices",
                Requirements: ["System must not be deployed"],
                ComplianceActions: ["Discontinue development", "Review for alternative approaches"]);
        }

        // Check for high-risk categories
        if (IsHighRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.High,
                Reasoning: "System falls under EU AI Act Annex III high-risk categories",
                Requirements: [
                    "Implement risk management system",
                    "Ensure data governance",
                    "Create technical documentation",
                    "Implement logging and record-keeping",
                    "Ensure transparency to users",
                    "Enable human oversight",
                    "Ensure accuracy, robustness, cybersecurity",
                    "Conduct conformity assessment"
                ],
                ComplianceActions: GetHighRiskActions(system));
        }

        // Check for limited risk (transparency obligations)
        if (IsLimitedRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Limited,
                Reasoning: "System has transparency obligations",
                Requirements: [
                    "Disclose AI interaction to users",
                    "Label AI-generated content where applicable",
                    "Inform about emotion recognition/biometric categorization"
                ],
                ComplianceActions: ["Implement disclosure mechanisms", "Update user interfaces"]);
        }

        // Minimal/no risk
        return new RiskClassification(
            Level: RiskLevel.Minimal,
            Reasoning: "System does not fall under regulated categories",
            Requirements: ["Consider voluntary codes of conduct"],
            ComplianceActions: ["Document risk assessment decision", "Monitor for regulatory changes"]);
    }

    private static bool IsProhibited(AISystemCharacteristics system)
    {
        return system.UseCase switch
        {
            AIUseCase.SocialScoring => system.DeployedBy == DeploymentContext.PublicAuthority,
            AIUseCase.SubliminalManipulation => true,
            AIUseCase.VulnerabilityExploitation => true,
            AIUseCase.FacialRecognitionScraping => true,
            AIUseCase.PredictivePolicing => system.BasedSolelyOnProfiling,
            AIUseCase.EmotionRecognition => system.Context is DeploymentContext.Workplace or DeploymentContext.Education
                                            && !system.ForMedicalOrSafetyPurposes,
            _ => false
        };
    }

    private static bool IsHighRisk(AISystemCharacteristics system)
    {
        return system.Category is
            AICategory.Biometrics or
            AICategory.CriticalInfrastructure or
            AICategory.Education or
            AICategory.Employment or
            AICategory.EssentialServices or
            AICategory.LawEnforcement or
            AICategory.MigrationAsylum or
            AICategory.JusticeDemocracy;
    }

    private static bool IsLimitedRisk(AISystemCharacteristics system)
    {
        return system.UseCase is
            AIUseCase.Chatbot or
            AIUseCase.EmotionRecognition or
            AIUseCase.DeepfakeGeneration or
            AIUseCase.ContentGeneration;
    }

    private static string[] GetHighRiskActions(AISystemCharacteristics system)
    {
        var actions = new List<string>
        {
            "Establish risk management system",
            "Document training data governance",
            "Create technical documentation per Annex IV",
            "Implement automatic logging",
            "Create instructions for use",
            "Design for human oversight"
        };

        if (system.Category == AICategory.Biometrics)
        {
            actions.Add("Conduct fundamental rights impact assessment");
            actions.Add("Register in EU AI database");
        }

        return [.. actions];
    }
}

public sealed record AISystemCharacteristics(
    AICategory Category,
    AIUseCase UseCase,
    DeploymentContext Context,
    DeploymentContext? DeployedBy = null,
    bool BasedSolelyOnProfiling = false,
    bool ForMedicalOrSafetyPurposes = false);

public sealed record RiskClassification(
    RiskLevel Level,
    string Reasoning,
    string[] Requirements,
    string[] ComplianceActions);

public enum RiskLevel { Minimal, Limited, High, Unacceptable }

public enum AICategory
{
    Biometrics,
    CriticalInfrastructure,
    Education,
    Employment,
    EssentialServices,
    LawEnforcement,
    MigrationAsylum,
    JusticeDemocracy,
    General
}

public enum AIUseCase
{
    SocialScoring,
    SubliminalManipulation,
    VulnerabilityExploitation,
    FacialRecognitionScraping,
    PredictivePolicing,
    EmotionRecognition,
    BiometricIdentification,
    CreditScoring,
    RecruitmentScreening,
    PerformanceMonitoring,
    Chatbot,
    DeepfakeGeneration,
    ContentGeneration,
    RecommendationSystem,
    GameAI,
    SpamFilter,
    Other
}

public enum DeploymentContext
{
    PublicAuthority,
    PrivateSector,
    Workplace,
    Education,
    Healthcare,
    LawEnforcement,
    General
}
csharp
namespace Security.AIGovernance;

/// <summary>
/// AI system risk classification and assessment.
/// </summary>
public sealed class AIRiskAssessment
{
    /// <summary>
    /// Classify AI system risk level based on characteristics.
    /// </summary>
    public static RiskClassification ClassifyRisk(AISystemCharacteristics system)
    {
        // Check for prohibited practices first
        if (IsProhibited(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Unacceptable,
                Reasoning: "System falls under EU AI Act prohibited practices",
                Requirements: ["System must not be deployed"],
                ComplianceActions: ["Discontinue development", "Review for alternative approaches"]);
        }

        // Check for high-risk categories
        if (IsHighRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.High,
                Reasoning: "System falls under EU AI Act Annex III high-risk categories",
                Requirements: [
                    "Implement risk management system",
                    "Ensure data governance",
                    "Create technical documentation",
                    "Implement logging and record-keeping",
                    "Ensure transparency to users",
                    "Enable human oversight",
                    "Ensure accuracy, robustness, cybersecurity",
                    "Conduct conformity assessment"
                ],
                ComplianceActions: GetHighRiskActions(system));
        }

        // Check for limited risk (transparency obligations)
        if (IsLimitedRisk(system))
        {
            return new RiskClassification(
                Level: RiskLevel.Limited,
                Reasoning: "System has transparency obligations",
                Requirements: [
                    "Disclose AI interaction to users",
                    "Label AI-generated content where applicable",
                    "Inform about emotion recognition/biometric categorization"
                ],
                ComplianceActions: ["Implement disclosure mechanisms", "Update user interfaces"]);
        }

        // Minimal/no risk
        return new RiskClassification(
            Level: RiskLevel.Minimal,
            Reasoning: "System does not fall under regulated categories",
            Requirements: ["Consider voluntary codes of conduct"],
            ComplianceActions: ["Document risk assessment decision", "Monitor for regulatory changes"]);
    }

    private static bool IsProhibited(AISystemCharacteristics system)
    {
        return system.UseCase switch
        {
            AIUseCase.SocialScoring => system.DeployedBy == DeploymentContext.PublicAuthority,
            AIUseCase.SubliminalManipulation => true,
            AIUseCase.VulnerabilityExploitation => true,
            AIUseCase.FacialRecognitionScraping => true,
            AIUseCase.PredictivePolicing => system.BasedSolelyOnProfiling,
            AIUseCase.EmotionRecognition => system.Context is DeploymentContext.Workplace or DeploymentContext.Education
                                            && !system.ForMedicalOrSafetyPurposes,
            _ => false
        };
    }

    private static bool IsHighRisk(AISystemCharacteristics system)
    {
        return system.Category is
            AICategory.Biometrics or
            AICategory.CriticalInfrastructure or
            AICategory.Education or
            AICategory.Employment or
            AICategory.EssentialServices or
            AICategory.LawEnforcement or
            AICategory.MigrationAsylum or
            AICategory.JusticeDemocracy;
    }

    private static bool IsLimitedRisk(AISystemCharacteristics system)
    {
        return system.UseCase is
            AIUseCase.Chatbot or
            AIUseCase.EmotionRecognition or
            AIUseCase.DeepfakeGeneration or
            AIUseCase.ContentGeneration;
    }

    private static string[] GetHighRiskActions(AISystemCharacteristics system)
    {
        var actions = new List<string>
        {
            "Establish risk management system",
            "Document training data governance",
            "Create technical documentation per Annex IV",
            "Implement automatic logging",
            "Create instructions for use",
            "Design for human oversight"
        };

        if (system.Category == AICategory.Biometrics)
        {
            actions.Add("Conduct fundamental rights impact assessment");
            actions.Add("Register in EU AI database");
        }

        return [.. actions];
    }
}

public sealed record AISystemCharacteristics(
    AICategory Category,
    AIUseCase UseCase,
    DeploymentContext Context,
    DeploymentContext? DeployedBy = null,
    bool BasedSolelyOnProfiling = false,
    bool ForMedicalOrSafetyPurposes = false);

public sealed record RiskClassification(
    RiskLevel Level,
    string Reasoning,
    string[] Requirements,
    string[] ComplianceActions);

public enum RiskLevel { Minimal, Limited, High, Unacceptable }

public enum AICategory
{
    Biometrics,
    CriticalInfrastructure,
    Education,
    Employment,
    EssentialServices,
    LawEnforcement,
    MigrationAsylum,
    JusticeDemocracy,
    General
}

public enum AIUseCase
{
    SocialScoring,
    SubliminalManipulation,
    VulnerabilityExploitation,
    FacialRecognitionScraping,
    PredictivePolicing,
    EmotionRecognition,
    BiometricIdentification,
    CreditScoring,
    RecruitmentScreening,
    PerformanceMonitoring,
    Chatbot,
    DeepfakeGeneration,
    ContentGeneration,
    RecommendationSystem,
    GameAI,
    SpamFilter,
    Other
}

public enum DeploymentContext
{
    PublicAuthority,
    PrivateSector,
    Workplace,
    Education,
    Healthcare,
    LawEnforcement,
    General
}

Model Cards and Documentation

模型卡片与文档

Model Card Template

模型卡片模板

yaml
model_card_template:
  model_details:
    name: ""
    version: ""
    type: "" # Classification, regression, generation, etc.
    developer: ""
    license: ""
    release_date: ""

  intended_use:
    primary_use_cases: []
    intended_users: []
    out_of_scope_uses: []

  factors:
    relevant_factors: []
    evaluation_factors: []

  metrics:
    performance_measures: []
    decision_thresholds: []
    variation_approaches: []

  evaluation_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  training_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  quantitative_analyses:
    unitary_results: []
    intersectional_results: []

  ethical_considerations:
    sensitive_use_cases: []
    known_limitations: []
    bias_mitigations: []

  caveats_recommendations:
    known_issues: []
    recommendations: []
    additional_testing: []
yaml
model_card_template:
  model_details:
    name: ""
    version: ""
    type: "" # Classification, regression, generation, etc.
    developer: ""
    license: ""
    release_date: ""

  intended_use:
    primary_use_cases: []
    intended_users: []
    out_of_scope_uses: []

  factors:
    relevant_factors: []
    evaluation_factors: []

  metrics:
    performance_measures: []
    decision_thresholds: []
    variation_approaches: []

  evaluation_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  training_data:
    datasets: []
    motivation: ""
    preprocessing: ""

  quantitative_analyses:
    unitary_results: []
    intersectional_results: []

  ethical_considerations:
    sensitive_use_cases: []
    known_limitations: []
    bias_mitigations: []

  caveats_recommendations:
    known_issues: []
    recommendations: []
    additional_testing: []

AI System Documentation

AI系统文档

csharp
namespace Security.AIGovernance;

/// <summary>
/// AI system documentation for compliance and transparency.
/// </summary>
public sealed record AISystemDocumentation
{
    // General Description
    public required string SystemName { get; init; }
    public required string Version { get; init; }
    public required string Description { get; init; }
    public required string IntendedPurpose { get; init; }
    public required RiskLevel RiskClassification { get; init; }
    public required DateTimeOffset DocumentDate { get; init; }

    // Provider Information
    public required OrganizationInfo Provider { get; init; }
    public required ContactInfo TechnicalContact { get; init; }
    public required ContactInfo ComplianceContact { get; init; }

    // Technical Specifications
    public required SystemArchitecture Architecture { get; init; }
    public required ModelSpecification Model { get; init; }
    public required DataSpecification TrainingData { get; init; }
    public required PerformanceMetrics Performance { get; init; }

    // Risk Management
    public required RiskAssessment Risks { get; init; }
    public required MitigationMeasures Mitigations { get; init; }
    public required HumanOversightDesign HumanOversight { get; init; }

    // Compliance
    public required ComplianceStatus Compliance { get; init; }
    public required List<AuditRecord> AuditHistory { get; init; }
    public required List<string> ApplicableRegulations { get; init; }
}

public sealed record OrganizationInfo(
    string Name,
    string Address,
    string Country,
    string RegistrationNumber);

public sealed record ContactInfo(
    string Name,
    string Email,
    string Phone);

public sealed record SystemArchitecture(
    string Description,
    List<string> Components,
    List<string> ExternalDependencies,
    List<string> IntegrationPoints);

public sealed record ModelSpecification(
    string ModelType,
    string Algorithm,
    string Framework,
    string TrainingApproach,
    DateTimeOffset LastTrainingDate);

public sealed record DataSpecification(
    string DataSources,
    long RecordCount,
    string DataTypes,
    string QualityMeasures,
    string BiasAssessment,
    bool ContainsSensitiveData,
    string SensitiveDataHandling);

public sealed record PerformanceMetrics(
    Dictionary<string, double> Metrics,
    string EvaluationMethodology,
    string LimitationsAndFailureModes);

public sealed record RiskAssessment(
    List<IdentifiedRisk> Risks,
    string OverallRiskLevel,
    DateTimeOffset AssessmentDate);

public sealed record IdentifiedRisk(
    string Description,
    string Likelihood,
    string Impact,
    string MitigationStatus);

public sealed record MitigationMeasures(
    List<string> TechnicalMeasures,
    List<string> OrganizationalMeasures,
    List<string> MonitoringMeasures);

public sealed record HumanOversightDesign(
    string OversightModel, // Human-in-the-loop, human-on-the-loop, human-in-command
    List<string> OversightMechanisms,
    List<string> OverrideCapabilities,
    string TrainingRequirements);

public sealed record ComplianceStatus(
    bool EUAIActCompliant,
    string ConformityAssessmentStatus,
    string CertificationStatus,
    DateTimeOffset LastComplianceReview);

public sealed record AuditRecord(
    DateTimeOffset Date,
    string AuditType,
    string Auditor,
    string Findings,
    string CorrectiveActions);
csharp
namespace Security.AIGovernance;

/// <summary>
/// AI system documentation for compliance and transparency.
/// </summary>
public sealed record AISystemDocumentation
{
    // General Description
    public required string SystemName { get; init; }
    public required string Version { get; init; }
    public required string Description { get; init; }
    public required string IntendedPurpose { get; init; }
    public required RiskLevel RiskClassification { get; init; }
    public required DateTimeOffset DocumentDate { get; init; }

    // Provider Information
    public required OrganizationInfo Provider { get; init; }
    public required ContactInfo TechnicalContact { get; init; }
    public required ContactInfo ComplianceContact { get; init; }

    // Technical Specifications
    public required SystemArchitecture Architecture { get; init; }
    public required ModelSpecification Model { get; init; }
    public required DataSpecification TrainingData { get; init; }
    public required PerformanceMetrics Performance { get; init; }

    // Risk Management
    public required RiskAssessment Risks { get; init; }
    public required MitigationMeasures Mitigations { get; init; }
    public required HumanOversightDesign HumanOversight { get; init; }

    // Compliance
    public required ComplianceStatus Compliance { get; init; }
    public required List<AuditRecord> AuditHistory { get; init; }
    public required List<string> ApplicableRegulations { get; init; }
}

public sealed record OrganizationInfo(
    string Name,
    string Address,
    string Country,
    string RegistrationNumber);

public sealed record ContactInfo(
    string Name,
    string Email,
    string Phone);

public sealed record SystemArchitecture(
    string Description,
    List<string> Components,
    List<string> ExternalDependencies,
    List<string> IntegrationPoints);

public sealed record ModelSpecification(
    string ModelType,
    string Algorithm,
    string Framework,
    string TrainingApproach,
    DateTimeOffset LastTrainingDate);

public sealed record DataSpecification(
    string DataSources,
    long RecordCount,
    string DataTypes,
    string QualityMeasures,
    string BiasAssessment,
    bool ContainsSensitiveData,
    string SensitiveDataHandling);

public sealed record PerformanceMetrics(
    Dictionary<string, double> Metrics,
    string EvaluationMethodology,
    string LimitationsAndFailureModes);

public sealed record RiskAssessment(
    List<IdentifiedRisk> Risks,
    string OverallRiskLevel,
    DateTimeOffset AssessmentDate);

public sealed record IdentifiedRisk(
    string Description,
    string Likelihood,
    string Impact,
    string MitigationStatus);

public sealed record MitigationMeasures(
    List<string> TechnicalMeasures,
    List<string> OrganizationalMeasures,
    List<string> MonitoringMeasures);

public sealed record HumanOversightDesign(
    string OversightModel, // Human-in-the-loop, human-on-the-loop, human-in-command
    List<string> OversightMechanisms,
    List<string> OverrideCapabilities,
    string TrainingRequirements);

public sealed record ComplianceStatus(
    bool EUAIActCompliant,
    string ConformityAssessmentStatus,
    string CertificationStatus,
    DateTimeOffset LastComplianceReview);

public sealed record AuditRecord(
    DateTimeOffset Date,
    string AuditType,
    string Auditor,
    string Findings,
    string CorrectiveActions);

Compliance Checklists

合规检查清单

EU AI Act High-Risk Compliance Checklist

欧盟AI法案高风险合规检查清单

yaml
eu_ai_act_high_risk_checklist:
  risk_management:
    - task: "Establish risk management system"
      status: "pending"
      evidence: ""
    - task: "Document known and foreseeable risks"
      status: "pending"
      evidence: ""
    - task: "Implement risk mitigation measures"
      status: "pending"
      evidence: ""
    - task: "Conduct testing for risk assessment"
      status: "pending"
      evidence: ""

  data_governance:
    - task: "Document training data sources"
      status: "pending"
      evidence: ""
    - task: "Implement data quality management"
      status: "pending"
      evidence: ""
    - task: "Conduct bias examination"
      status: "pending"
      evidence: ""
    - task: "Verify data representativeness"
      status: "pending"
      evidence: ""

  technical_documentation:
    - task: "Create Annex IV compliant documentation"
      status: "pending"
      evidence: ""
    - task: "Document system architecture"
      status: "pending"
      evidence: ""
    - task: "Document training methodology"
      status: "pending"
      evidence: ""
    - task: "Document performance metrics"
      status: "pending"
      evidence: ""

  record_keeping:
    - task: "Implement automatic logging"
      status: "pending"
      evidence: ""
    - task: "Log user interactions"
      status: "pending"
      evidence: ""
    - task: "Define retention periods"
      status: "pending"
      evidence: ""

  transparency:
    - task: "Create instructions for use"
      status: "pending"
      evidence: ""
    - task: "Document capabilities and limitations"
      status: "pending"
      evidence: ""
    - task: "Specify accuracy levels"
      status: "pending"
      evidence: ""

  human_oversight:
    - task: "Design oversight mechanisms"
      status: "pending"
      evidence: ""
    - task: "Implement override capabilities"
      status: "pending"
      evidence: ""
    - task: "Define operator training requirements"
      status: "pending"
      evidence: ""

  accuracy_robustness_cybersecurity:
    - task: "Validate performance metrics"
      status: "pending"
      evidence: ""
    - task: "Test for robustness"
      status: "pending"
      evidence: ""
    - task: "Conduct security assessment"
      status: "pending"
      evidence: ""

  conformity_assessment:
    - task: "Complete self-assessment or third-party assessment"
      status: "pending"
      evidence: ""
    - task: "Prepare EU declaration of conformity"
      status: "pending"
      evidence: ""
    - task: "Register in EU database (if applicable)"
      status: "pending"
      evidence: ""
yaml
eu_ai_act_high_risk_checklist:
  risk_management:
    - task: "Establish risk management system"
      status: "pending"
      evidence: ""
    - task: "Document known and foreseeable risks"
      status: "pending"
      evidence: ""
    - task: "Implement risk mitigation measures"
      status: "pending"
      evidence: ""
    - task: "Conduct testing for risk assessment"
      status: "pending"
      evidence: ""

  data_governance:
    - task: "Document training data sources"
      status: "pending"
      evidence: ""
    - task: "Implement data quality management"
      status: "pending"
      evidence: ""
    - task: "Conduct bias examination"
      status: "pending"
      evidence: ""
    - task: "Verify data representativeness"
      status: "pending"
      evidence: ""

  technical_documentation:
    - task: "Create Annex IV compliant documentation"
      status: "pending"
      evidence: ""
    - task: "Document system architecture"
      status: "pending"
      evidence: ""
    - task: "Document training methodology"
      status: "pending"
      evidence: ""
    - task: "Document performance metrics"
      status: "pending"
      evidence: ""

  record_keeping:
    - task: "Implement automatic logging"
      status: "pending"
      evidence: ""
    - task: "Log user interactions"
      status: "pending"
      evidence: ""
    - task: "Define retention periods"
      status: "pending"
      evidence: ""

  transparency:
    - task: "Create instructions for use"
      status: "pending"
      evidence: ""
    - task: "Document capabilities and limitations"
      status: "pending"
      evidence: ""
    - task: "Specify accuracy levels"
      status: "pending"
      evidence: ""

  human_oversight:
    - task: "Design oversight mechanisms"
      status: "pending"
      evidence: ""
    - task: "Implement override capabilities"
      status: "pending"
      evidence: ""
    - task: "Define operator training requirements"
      status: "pending"
      evidence: ""

  accuracy_robustness_cybersecurity:
    - task: "Validate performance metrics"
      status: "pending"
      evidence: ""
    - task: "Test for robustness"
      status: "pending"
      evidence: ""
    - task: "Conduct security assessment"
      status: "pending"
      evidence: ""

  conformity_assessment:
    - task: "Complete self-assessment or third-party assessment"
      status: "pending"
      evidence: ""
    - task: "Prepare EU declaration of conformity"
      status: "pending"
      evidence: ""
    - task: "Register in EU database (if applicable)"
      status: "pending"
      evidence: ""

NIST AI RMF Implementation Checklist

NIST AI RMF实施检查清单

yaml
nist_ai_rmf_checklist:
  govern:
    - task: "Establish AI governance policies"
      status: "pending"
    - task: "Define accountability structures"
      status: "pending"
    - task: "Create risk management procedures"
      status: "pending"
    - task: "Establish stakeholder engagement processes"
      status: "pending"
    - task: "Map legal and regulatory requirements"
      status: "pending"

  map:
    - task: "Document intended purpose and use cases"
      status: "pending"
    - task: "Classify AI system by risk category"
      status: "pending"
    - task: "Identify potential impacts and affected parties"
      status: "pending"
    - task: "Document data and model dependencies"
      status: "pending"
    - task: "Identify and enumerate risks"
      status: "pending"

  measure:
    - task: "Define risk metrics and indicators"
      status: "pending"
    - task: "Conduct bias and fairness testing"
      status: "pending"
    - task: "Evaluate model performance"
      status: "pending"
    - task: "Implement continuous monitoring"
      status: "pending"
    - task: "Schedule independent assessments"
      status: "pending"

  manage:
    - task: "Prioritize risks by severity"
      status: "pending"
    - task: "Implement risk mitigations"
      status: "pending"
    - task: "Develop contingency and rollback plans"
      status: "pending"
    - task: "Document residual risks and acceptances"
      status: "pending"
    - task: "Establish ongoing communication processes"
      status: "pending"
yaml
nist_ai_rmf_checklist:
  govern:
    - task: "Establish AI governance policies"
      status: "pending"
    - task: "Define accountability structures"
      status: "pending"
    - task: "Create risk management procedures"
      status: "pending"
    - task: "Establish stakeholder engagement processes"
      status: "pending"
    - task: "Map legal and regulatory requirements"
      status: "pending"

  map:
    - task: "Document intended purpose and use cases"
      status: "pending"
    - task: "Classify AI system by risk category"
      status: "pending"
    - task: "Identify potential impacts and affected parties"
      status: "pending"
    - task: "Document data and model dependencies"
      status: "pending"
    - task: "Identify and enumerate risks"
      status: "pending"

  measure:
    - task: "Define risk metrics and indicators"
      status: "pending"
    - task: "Conduct bias and fairness testing"
      status: "pending"
    - task: "Evaluate model performance"
      status: "pending"
    - task: "Implement continuous monitoring"
      status: "pending"
    - task: "Schedule independent assessments"
      status: "pending"

  manage:
    - task: "Prioritize risks by severity"
      status: "pending"
    - task: "Implement risk mitigations"
      status: "pending"
    - task: "Develop contingency and rollback plans"
      status: "pending"
    - task: "Document residual risks and acceptances"
      status: "pending"
    - task: "Establish ongoing communication processes"
      status: "pending"

References

参考资料

  • EU AI Act Details: See
    references/eu-ai-act-requirements.md
    for full regulatory text mapping
  • NIST AI RMF: See
    references/nist-ai-rmf-profiles.md
    for sector-specific profiles
  • Model Cards: See
    references/model-card-examples.md
    for completed examples
  • 欧盟AI法案详情: 详见
    references/eu-ai-act-requirements.md
    获取完整法规文本映射
  • NIST AI RMF: 详见
    references/nist-ai-rmf-profiles.md
    获取行业特定配置文件
  • 模型卡片: 详见
    references/model-card-examples.md
    获取已完成示例

Related Skills

相关技能

  • threat-modeling
    - Security threat analysis for AI systems
  • devsecops-practices
    - Integrating AI governance into pipelines
  • vulnerability-management
    - Managing AI system vulnerabilities

Last Updated: 2025-12-26
  • threat-modeling
    - AI系统安全威胁分析
  • devsecops-practices
    - 将AI治理整合到流水线中
  • vulnerability-management
    - 管理AI系统漏洞

最后更新时间: 2025-12-26