AI & Compliance
AI compliance ensures that artificial intelligence systems align with laws, regulations, and ethical requirements. It includes formal standards, documentation, tiered risk classification, audits, explainability, human oversight, incident management, and conformance checks. These elements together provide a framework for trustworthy AI.
Regulations & Standards
Compliance begins with adherence to legal and technical frameworks across regions and industries.
| Framework | Examples | Role |
|---|---|---|
| AI-Specific Laws | EU AI Act, U.S. Executive Orders | Set binding AI obligations |
| Data Protection | GDPR, CCPA | Protect personal data use |
| Technical Standards | ISO/IEC, NIST AI RMF | Provide structured best practices |
Documentation
Accurate documentation demonstrates compliance and provides transparency for auditors and users.
| Documentation Type | Examples | Purpose |
|---|---|---|
| Model Cards | Performance metrics, limitations | Communicate intended use |
| Datasheets | Provenance, bias checks | Improve dataset transparency |
| Audit Logs | System activity records | Enable traceability |
Risk Tiers
Compliance frameworks often classify AI systems by risk level to determine requirements.
| Risk Tier | Examples | Compliance Requirements |
|---|---|---|
| Unacceptable | Social scoring, manipulative AI | Prohibited outright |
| High-Risk | Healthcare AI, hiring systems | Strict oversight and documentation |
| Limited / Low-Risk | Chatbots, recommender systems | Transparency, disclosure |
Audits
Audits validate compliance and ensure systems perform as intended under regulation.
| Audit Type | Examples | Impact |
|---|---|---|
| Internal Audits | Self-assessment, compliance reviews | Identify issues early |
| Third-Party Audits | Independent assessments | Increase credibility and trust |
| Regulatory Audits | Government oversight checks | Enforce legal compliance |
Explainability
Explainability requirements ensure users can understand how AI systems make decisions.
| Method | Examples | Compliance Role |
|---|---|---|
| Post-Hoc Analysis | LIME, SHAP explanations | Clarify black-box outputs |
| Transparent Models | Decision trees, interpretable ML | Built-in explainability |
| User Communication | Plain-language justifications | Support end-user trust |
Human-in-the-Loop
Human oversight is a key compliance safeguard to ensure accountability and prevent automation bias.
| Oversight Mechanism | Examples | Benefit |
|---|---|---|
| Approval Workflows | Manual review of critical decisions | Adds human judgment |
| Override Controls | Kill-switch, escalation protocols | Ensure human authority |
| Ongoing Training | Human reviewers trained in AI | Improve oversight quality |
Incidents
Incident management addresses failures, misuse, or harmful AI outcomes.
| Incident Type | Examples | Response |
|---|---|---|
| Operational Failures | AI misdiagnosis, system outages | Incident reports, corrective action |
| Security Breaches | Adversarial attacks, data leaks | Breach notifications, remediation |
| Ethical Violations | Discriminatory outcomes | Remediation plans, retraining |
Conformance
Conformance testing ensures AI systems continuously meet compliance requirements.
| Conformance Type | Examples | Role |
|---|---|---|
| Pre-Deployment | Testing against standards | Verify readiness |
| Continuous Monitoring | Drift detection, compliance dashboards | Sustain adherence |
| Certification | ISO/IEC AI certification | Provide formal assurance |
FAQ
Why are risk tiers important in AI regulation?
Risk tiers are important because they classify AI systems by potential impact, ranging from unacceptable risk (banned) to high-risk (strictly regulated) to low-risk (limited obligations). This helps apply proportional safeguards.
What kind of documentation is required for AI compliance?
Required documentation often includes model cards, datasheets for datasets, audit logs, and transparency reports, which provide evidence of responsible development and operation.
Why is explainability a compliance requirement?
Explainability is required because regulators and users need to understand how AI systems reach decisions, which reduces the risks of black-box decision-making and increases trust.
What role does human-in-the-loop play in compliance?
Human-in-the-loop provides oversight by allowing humans to review, override, or approve AI decisions, ensuring accountability and preventing over-reliance on automated outputs.
What does accountability mean in AI governance?
Accountability means ensuring there are clear mechanisms to trace decisions, assign responsibility, and provide remedies when AI systems cause harm.