Tier 2: Evidence-grade controls

Bias, fairness, and non-discrimination controls

Documented measures to detect, prevent, and remediate discriminatory outcomes in AI systems, especially where decisions affect people, access, or eligibility.

Board-defensible evidence

• Defined fairness objectives tied to the use case, including which protected classes and stakeholder groups are in scope, and why the chosen fairness approach is appropriate for the business context.
• Testing artifacts showing pre-deployment bias evaluation, what datasets were used, what metrics were computed, and how results were reviewed and approved by risk and compliance.
• Ongoing monitoring records showing fairness metrics over time, alert thresholds, and evidence of investigation when drift or disparate impact signals appear.
• Remediation logs showing what changes were made (data rebalancing, threshold adjustments, model changes, policy changes), who authorized them, and what post-fix validation confirmed improvement.
• External or second-line review evidence (legal, compliance, internal audit, or independent reviewer) confirming testing and monitoring are consistent with policy and that exceptions are justified and time-bound.

Why this matters

Discrimination claims convert quickly into regulatory scrutiny and litigation, and the defense depends on what you tested, what you monitored, and what you fixed.