Book Review: AI Fairness

Essay by Will Dzierson · June 2, 2025

Just finished AI Fairness: Designing Equal Opportunity Algorithms by Derek Leben, and it’s a must-read for anyone shaping AI-driven products. If you’re in the business of building systems that influence real lives—think credit approvals, hiring algorithms, or content recommendations—this book is your ethical compass.

Why This Book Matters:

Leben doesn’t just theorize; he offers a practical framework grounded in moral philosophy, particularly John Rawls’ theory of justice. He introduces a “theory of algorithmic justice” that helps us navigate the murky waters where fairness metrics collide and trade-offs are inevitable.

Real-World Implications:

  • Apple Card Controversy: Remember when Apple’s credit card was accused of gender bias? Leben dissects this case, showing how even well-intentioned algorithms can perpetuate inequality if not carefully audited.
  • COMPAS Recidivism Tool: He examines this criminal justice algorithm to highlight how different fairness metrics (like equal false positive rates vs. equal accuracy) can lead to conflicting outcomes, forcing us to make tough ethical choices.
  • Generative AI Gone Awry: Leben discusses instances where image generators, in an attempt to be fair, produced absurd results—like depicting historical figures with anachronistic diversity—underscoring the importance of context-aware fairness interventions.

Key Takeaways for Practitioners:

  • Fairness Isn’t One-Size-Fits-All: You can’t satisfy all fairness metrics simultaneously. Choose the one that aligns with your product’s ethical goals and be prepared to justify it.
  • Transparency is Crucial: Your AI system should be able to explain its decisions, especially regarding which features influenced outcomes, to ensure accountability.
  • Regular Audits: Continuously evaluate your models for fairness, as societal norms and data distributions evolve over time.
  • Design with Empathy: Understand the lived experiences of those affected by your AI systems. Engage with diverse user groups to inform your fairness criteria.

Final Thoughts:

This book is more than an academic treatise; it’s a call to action for all of us involved in AI product development. It challenges us to think deeply about the societal impact of our work and provides tools to do better.