The Alignment Problem by Brian Christian

The book received positive reviews from critics. Christian discusses the normative challenges associated with effective altruism and existential risk, including the work of philosophers Toby Ord and William MacAskill who are trying to devise human and machine strategies for navigating the alignment problem as effectively as possible. Of particular importance is inverse reinforcement learning, a broad approach for machines to learn the objective function of a human or another agent. The third section covers training AI through the imitation of human or machine behavior, as well as philosophical debates such as between possibilism and actualism that imply different ideal behavior for AI systems. He calls the DeepMind AlphaGo and AlphaZero systems 'perhaps the single most impressive achievement in automated curriculum design.' He also highlights the importance of curiosity, in which reinforcement learners are intrinsically motivated to explore their environment, rather than exclusively seeking the external reward. In the second section, Christian similarly interweaves the history of the psychological study of reward, such as behaviorism and dopamine, with the computer science of reinforcement learning, in which AI systems need to develop policy ('what to do') in the face of a value function ('what rewards or punishment to expect'). The lack of transparency makes it difficult to know where the system is going right and where it is going wrong. One of AI's main alignment challenges is its black box nature (inputs and outputs are identifiable but the transformation process in between is undetermined).

He tells the story of Julia Angwin, a journalist whose ProPublica investigation of the COMPAS algorithm, a tool for predicting recidivism among criminal defendants, led to widespread criticism of its accuracy and bias towards certain demographics. In the first section, Christian interweaves discussions of the history of artificial intelligence research, particularly the machine learning approach of artificial neural networks such as the Perceptron and AlexNet, with examples of how AI systems can have unintended behavior. Each section covers researchers and engineers working on different challenges in the alignment of artificial intelligence with human values. The book is divided into three sections: Prophecy, Agency, and Normativity. It is based on numerous interviews with experts trying to build artificial intelligence systems, particular machine learning systems, that are aligned with human values. The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian.

The Alignment Problem by Brian Christian