Case studies of real-world machine learning failures highlight ethical and societal risks for readers
Expert interviews with leading researchers and practitioners provide insider perspectives on the alignment problem
Interdisciplinary approach examines both technical and cultural dimensions of AI development
Hopeful yet sobering narrative offers insights into future solutions and human-AI collaboration
Summarized by Shop
Finalist for the Los Angeles Times Book Prize A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them.
Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing.