The AI Bias Playbook
Understanding algorithmic bias: where it comes from, why it matters, and how to detect, mitigate, and prevent it.
Complete Series Progress
7 parts • Hover to view individual progress
★Key Takeaways
About This Series
Algorithmic bias isn't a bug—it's a systemic feature learned from biased data, encoded by biased assumptions, and amplified by our models. This 7-part series deconstructs the anatomy of bias (data bias, measurement bias, algorithmic bias), explores real-world consequences (Amazon hiring tool, digital redlining), and provides technical and organizational strategies to build fair AI systems.
All Parts
The AI Bias Playbook (Part 1): What We Talk About When We Talk About 'Bias'
Understanding the human cost of unexamined algorithms and the three primary sources of AI bias.
The AI Bias Playbook (Part 2): The Legal & Reputational Nightmare
Understanding the legal liability, massive fines, and $70 billion market consequences of biased AI systems.
The AI Bias Playbook (Part 3): A Leader's Guide to 'Fairness Metrics'
Understanding competing definitions of fairness and choosing the right metric as a foundational policy decision.
The AI Bias Playbook (Part 4): Guardrails (Part 1) — Pre-Deployment Testing
Your first and most critical line of defense—creating audit-ready evidence before launch.
The AI Bias Playbook (Part 5): Guardrails (Part 2) — 'At-Runtime' Testing
Dynamic, real-time filters to protect generative AI from unpredictable and malicious user inputs.
The AI Bias Playbook (Part 6): Guardrails (Part 3) — Continuous Monitoring
Why launch-and-forget is a catastrophic mistake and how fairness dashboards provide early-warning systems.
The AI Bias Playbook (Part 7): Building a Culture of Fairness
The three-legged stool of defensible AI governance—People, Process, and Platform working in harmony.