The Algorithmic Fairness AI Handbook

build AI that does not discriminate

Policy professionals are under pressure to use AI, but the work still has to be correct, on-brand, and defensible. Without a clear method, you either avoid the tools or use them sloppily and lose trust. The cost shows up fast - wasted time, inconsistent quality, and avoidable risk (hallucinations, privacy leaks, and over-automation). Tasks like 'What fairness means - metrics, tradeoffs, and context' and 'How bias enters systems - data, labels, and incentives' become harder, not easier, when the process is fuzzy. This handbook gives you a step-by-step system you can turn directly into a chapter-by-chapter TOC: how to set goals, choose tools, write prompts that include the right context, verify outputs, and reuse templates. Expect ready-to-use checklists and artifacts for core outcomes like what fairness means - metrics, tradeoffs, and context, how bias enters systems - data, labels, and incentives, and audit playbook - tests, documentation, and red flags.

Inside this book

• What fairness means - metrics, tradeoffs, and context
• How bias enters systems - data, labels, and incentives
• Audit playbook - tests, documentation, and red flags
• Governance - approvals, monitoring, and accountability
• Communication - explaining results to non-technical stakeholders
• Tool setup and guardrails - data boundaries, privacy, and audit trails
• Templates you can reuse - prompts, checklists, and scoring rubrics
• Measurement - track time saved and quality improvements over 30 days

By Valentin Nagacevschi