AI Governance & Ethics

A mature governance framework typically spans across multiple domains of an AI strategy.

• • Safety & Security: Protecting the system from adversarial attacks (e.g., prompt injection) and preventing dangerous outputs.
• • Fairness & Bias: Ensuring models handle data equitably across different demographics without reinforcing historical prejudices.
• • Transparency & Explainability: Documenting how models are trained and providing mechanisms to understand how specific decisions were reached.
• • Privacy & Data Rights: Governing what data can be used for training, ensuring compliance with regulations like GDPR or CCPA.

The rapid capabilities of GenAI have accelerated regulation. The EU AI Act takes a risk-based approach, banning systems that pose "unacceptable risk" (like social scoring) and heavily regulating "high-risk" systems. Meanwhile, frameworks like the NIST AI Risk Management Framework guide voluntary compliance in the US.