The Alignment Problem: Machine Learning and Human Values

Christian opens the Conclusion of the book with an account of his Christmas Eve spent with his wife at his father’s house. Christian awakens sweating heavily due to overheating in his room caused by a misaligned thermostat in another room, open to the colder air of the house. This incident illustrates the practical risks of relying on systems without fully understanding how they function and their potential hazards.

Christian then follows with a chapter-by-chapter overview of the book. Chapter 1 of the book discusses the importance of representative training data in model development, with a focus on the shift toward more inclusive data sets in face recognition technology. Despite progress in consumer technology, studies reveal bias against racial minorities already subject to high surveillance. The chapter also compares these data representation issues with longstanding biases in medical trials predominantly conducted on men, exposing systemic issues in both fields. These discussions, emphasize the broader challenges to ensuring that AI systems and medical practices are inclusive and fair.

Chapter 2 discusses the use of risk assessment tools in criminal justice, pointing to the fact that they often rely on problematic proxies like rearrest and reconviction rates instead of actual recidivism. These tools may reinforce biases, especially if certain demographics are more likely to be arrested or convicted.