10 Bias, privacy and trust in AI systems
This chapter covers
- The four fundamental failure modes that threaten production LLM systems
- Implementing a four-layer defense architecture that prevents bias, safety violations, and privacy breaches
- Building comprehensive bias detection and mitigation systems using proven techniques
- Designing privacy protection systems that comply with HIPAA and GDPR requirements
- Creating a production-ready medical AI assistant with enterprise-grade safety measures
In 2018, Amazon scrapped a recruiting tool that had been in development for four years. The AI system, designed to review resumes and rank candidates, had taught itself to systematically discriminate against women. It penalized resumes that included words like "women's" (as in "women's chess club captain") and downgraded graduates from all-women's colleges.
The problem wasn't a bug in the code—it was the AI working exactly as designed. Trained on a decade of Amazon's hiring data, which was predominantly male due to the tech industry's gender imbalance, the system learned that male candidates were preferable and codified this bias into its scoring algorithm.
10.1 The responsible AI imperative
10.1.1 Regulatory pressure is accelerating
10.1.2 User expectations have shifted
10.1.3 Business risks have multiplied
10.1.4 Real examples of AI bias in production
10.1.5 The four failure modes
10.1.6 The responsible AI defense system
10.2 Data layer: Where bias begins
10.2.1 The fine-tuning bias trap
10.2.2 Detecting bias in chat logs
10.2.3 The name experiment
10.2.4 Three proven bias mitigation strategies
10.3 Model layer: Where bias evolves
10.3.1 Why this matters for open-source
10.3.2 Example: Adding fairness to a LoRA fine-tuning loop
10.3.3 ANTHROPIC’S constitutional AI: LLM-as-judge at training scale
10.4 Safety layer: Your last line of defense
10.4.1 Multi-layered safety architecture
10.4.2 Layer 3: Enhanced safety with commercial APIs
10.5 Privacy layer: Protecting personal data
10.5.1 Why LLM privacy failures are uniquely dangerous
10.5.2 Building Sensitive Data Detection
10.5.3 Understanding HIPAA: Healthcare privacy protection
10.5.4 Understanding GDPR: European data protection
10.6 Real-world project: SafeMedAssist
10.6.1 Why a medical AI assistant?
10.6.2 Professional testing with LangTest
10.6.3 Production Deployment Considerations
10.6.4 The business case for responsible AI
10.7 Summary