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AI Ethics, Compliance & Governance

Navigate the complex landscape of responsible AI. Learn to build systems that are fair, transparent, and compliant with global regulations (EU AI Act, NIST). Master techniques for bias detection, red-teaming, and implementing robust governance frameworks for enterprise AI.

AI Ethics, Compliance & Governance (Master Class)

A — FOUNDATIONS OF RESPONSIBLE AI

A1. Core Ethical Principles

  • Fairness: Defining and measuring algorithmic bias (demographic parity, equal opportunity).
  • Transparency: Explainability (XAI) vs. interpretability. SHAP, LIME, and attention maps.
  • Accountability: Human-in-the-loop (HITL) vs. Human-on-the-loop (HOTL) systems.
  • Privacy: Differential privacy, federated learning, and data minimization.
  • Safety: Robustness against adversarial attacks (jailbreaking, prompt injection).

A2. The Global Regulatory Landscape

  • EU AI Act: Risk-based classification (Unacceptable, High, Limited, Minimal). Compliance requirements for GPAI (General Purpose AI).
  • NIST AI Risk Management Framework (RMF): Map, Measure, Manage, Govern.
  • US Executive Order on AI: Key mandates for safety testing and watermarking.
  • GDPR & AI: Automated decision-making rights, right to explanation, and data subject requests.
  • Copyright Law: Fair use in training data (NYT vs OpenAI), output ownership.

B — TECHNICAL COMPLIANCE & GOVERNANCE

B1. Data Governance

  • Data Lineage: Tracking data provenance from source to model.
  • Consent Management: Managing opt-outs and "Right to be Forgotten" in trained models (Machine Unlearning).
  • Bias Mitigation in Datasets: Re-sampling, re-weighting, and synthetic data augmentation.

B2. Model Governance (MLOps + Governance)

  • Model Cards: Standardized documentation (intended use, limitations, training data).
  • Version Control & Audit Trails: Reproducibility in regulated industries (Finance, Healthcare).
  • Red Teaming: Methodologies for stress-testing models against harmful outputs.
  • Guardrails: Implementing input/output filters (e.g., NeMo Guardrails, Llama Guard).

B3. Enterprise Implementation

  • AI Ethics Committees: Structure, charter, and decision-making power.
  • Risk Assessment Matrices: Quantifying AI risk (Impact vs. Likelihood).
  • Procurement Policies: Evaluating third-party AI vendors for compliance.
  • Incident Response: Protocols for AI hallucinations or safety failures in production.

C — FUTURE OF AI GOVERNANCE

C1. Emerging Challenges

  • Deepfakes & Disinformation: Watermarking standards (C2PA) and detection technologies.
  • Agentic AI Risks: Liability when autonomous agents take real-world actions.
  • Superalignment: Theoretical frameworks for controlling superintelligent AI.
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