Fairness Audit Framework Playbook

A practical, end-to-end framework for auditing and mitigating algorithmic bias in AI-driven decision systems.

This playbook provides structured tools to help organizations identify, measure, and address fairness risks across the machine learning lifecycle, with a particular emphasis on intersectional analysis and statistical rigor.

Who This Is For

• Machine Learning and Data Science teams
• Product and Engineering leaders
• HR, compliance, and legal stakeholders
• AI governance and ethics practitioners

Core Components

1. Historical Context Assessment Tool Identifies historically embedded risks and structural inequities relevant to the application domain.

2. Fairness Definition Selection Tool
   Guides teams in selecting appropriate fairness definitions based on domain-specific harms and trade-offs.

3. Bias Source Identification Tool
   Applies a comprehensive taxonomy covering data, modeling, evaluation, and deployment bias.

4. Fairness Metrics and Validation Tool
   Translates fairness definitions into statistically validated metrics with uncertainty estimation and robustness checks.

The tools are designed to be used sequentially:

1. Start with historical and domain context
2. Define fairness goals and acceptable trade-offs
3. Identify and prioritize sources of bias
4. Measure, validate, and monitor fairness outcomes

A complete walkthrough is provided in the Case Study.