How do I audit AI agent decisions for bias?

Auditing AI agent decisions for bias requires logging every action with sufficient context to detect systematic patterns — because individual biased decisions look normal in isolation, but aggregate patterns reveal discrimination in hiring, lending, content moderation, and customer treatment.

Bias auditing requirements for AI agents:

• Decision logging: Every agent decision must be logged with the input data, decision rationale, and outcome — not just the final action
• Demographic analysis: Statistical analysis across protected categories (race, gender, age, disability) to detect disparate impact
• Counterfactual testing: "Would the agent have made the same decision if the applicant's name was different?" — requires replay capability
• Temporal analysis: Detecting bias drift over time as the agent's behavior changes

Exogram's Trust Ledger provides the foundation for bias auditing. Every evaluation is logged with the full action payload, policy results, and state context. This data can be exported to your analytics platform for demographic analysis. SHA-256 state hashing enables exact replay for counterfactual testing. The governance layer doesn't detect bias directly — but it generates the comprehensive audit data that makes bias detection possible.