“The Alignment Problem should be required reading for anyone influencing policy where algorithms are in play – which is everywhere. But unlike much required reading, the book is a delight to read, a playful romp through personalities and relatable snippets of science history that put the choices of our present moment into context.”
Jennifer Pahlka, founder of Code for America and former deputy CTO of the United States
In The Alignment Problem: Machine Learning and Human Values, Brian Christian examines the growing gap between human intent and machine behavior, a central challenge in modern artificial intelligence.
Machine learning influences criminal sentencing, hiring, healthcare, and autonomous vehicles, raising urgent questions about whether these systems reflect human values.
Christian explains that machine learning relies on models that learn from data instead of following explicit rules. This approach, while powerful, often produces unintended results. Systems may meet their objectives but still reinforce bias and reduce accountability.
Through many examples, Christian shows how flawed algorithms reveal the depth of the problem.
He also profiles researchers in computer science, psychology, and philosophy who are working to align AI systems with human goals. Their work focuses on making these systems more transparent and fair, aiming to understand how machines process the nuanced and often conflicting signals they receive.
The Alignment Problem explores how machine learning has developed alongside human values and real-world consequences. Christian looks at how technical work connects with ethical questions, showing what progress has been made and what challenges remain.
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