Policy Pillar: The "What" and "Why" - Setting the Rules of the Road
Purpose: Defines the organization's binding commitments, standards, and expectations for responsible AI development, deployment, and use.
Core Components:
Risk Classification Schema: A clear system for categorizing AI applications based on potential impact (e.g., High-Risk: Hiring, Credit Scoring, Critical Infrastructure; Medium-Risk: Internal Process Automation; Low-Risk: Basic Chatbots). This dictates the level of governance scrutiny. (e.g., Align with NIST AI RMF or EU AI Act categories).
Core Mandatory Requirements: Specific, non-negotiable obligations applicable to all AI projects. Examples:
Human Oversight: Define acceptable levels of human-in-the-loop, on-the-loop, or review for different risk classes.
Fairness & Bias Mitigation: Requirements for impact assessments, testing metrics (e.g., demographic parity difference, equal opportunity difference), and mitigation steps.
Transparency & Explainability: Minimum standards for model documentation (e.g., datasheets, model cards), user notifications, and explainability techniques required based on risk.
Robustness, Safety & Security: Requirements for adversarial testing, accuracy thresholds, drift monitoring, and secure development/deployment practices (e.g., OWASP AI Security & Privacy Guide).
Privacy: Compliance with relevant data protection laws (GDPR, CCPA, etc.), data minimization, and purpose limitation for training data.
Accountability & Traceability: Mandate for audit trails tracking model development, data lineage, decisions, and changes.
Read More: From Principles to Playbook: Build an AI-Governance Framework in 30 Days
