What happens when AI learns to enforce rules from systems designed to be unfair?
AI is already auditing tax returns, detecting fraud, assessing credit risk, and making hiring decisions. But every enforcement algorithm being built today is trained on historical data shaped by a fundamental problem: structural asymmetry.
One party sets the rules, interprets them, and faces no consequences for mistakes. The other can only comply or face penalties. Mistakes are remembered forever. Corrections are forgotten immediately.
We're teaching AI to replicate this pattern and scale it to millions of decisions per second.
The Fairness Algorithm exposes how power imbalances become permanent in enforcement systems and provides the blueprint to fix them before AI makes them irreversible.
Inside this book:
Drawing on thirteen years of experience as both auditor and auditee, Ruben Lopez presents two groundbreaking frameworks:
The Lopez Theory of Asymmetric Enforcement (LTAE) diagnoses why enforcement becomes coercive through six axioms rooted in game theory, revealing the Stackelberg dynamics, grim triggers, and Bayesian biases that trap both sides in escalation.
The Lopez Audit Anchor Model (LAAM) provides the corrective: a working framework that teaches AI systems to enforce rules fairly through strategic memory (anchors that decay with verified compliance), dynamic fairness (evolving consequences that reward correction), and transparent accountability (logged discretion that prevents bias).
This isn't just audit reform. It's a blueprint for building ethical AI in any domain where power is asymmetric: credit scoring, healthcare enforcement, hiring algorithms, content moderation, and beyond.
What you'll discover:
- Why traditional AI fairness research misses structural power imbalances
- How game theory reveals the rules that make enforcement coercive by design
- The three mechanisms that transform AI from efficient enforcer to fair arbitrator
- Real case studies showing LAAM in action
- An interactive simulator where you can experience both sides of the audit game
- Implementation pathways for developers, regulators, and policymakers
- How LAAM principles apply to AI alignment and superintelligence safety
For readers who want to:
- Understand how AI enforcement systems actually work
- Build fairness into algorithms at the architectural level
- Reform audit and compliance systems that feel broken
- Explore the intersection of game theory, AI ethics, and institutional design
- Prepare for a future where machines enforce rules we can't oversee
Includes:
- Complete LAAM framework with mathematical foundations
- Working Python implementation (open-source)
- Public simulator for hands-on learning
- Teaching curriculum for experiential education
- Full academic dissertation (published on SSRN/OSF)
Whether you're an AI developer, policy researcher, business owner, or concerned citizen, The Fairness Algorithm shows you how to recognize asymmetric enforcement and resist it before it becomes permanent.
AI will either become the fairest arbitrator humanity has ever built, or the most efficient oppressor. Which future we get depends on what we teach AI now.
