Publications

Machine learning turns on one simple trick: Split the data into training and test sets. Anything goes on the training set. Rank models on the test set and let model builders compete. Call it a benchmark. Machine learning researchers cherish a good tradition of lamenting the apparent shortcomings of benchmarks. Critics argue that static test sets and metrics promote narrow research objectives, stifling more creative scientific pursuits. Benchmarks also incentivize gaming; in fact, Goodhart's Law cautions against applying competitive pressure to statistical measurement. Over time, researchers may overfit to benchmarks, building models that exploit data artifacts. As a result, test set performance draws a skewed picture of model capabilities that deceives us—especially when comparing humans and machines. To top off the list of issues, there are a slew of reasons why things don't transfer well from benchmarks to the real world.

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility.• Introduces the technical and normative foundations of fairness in automated decision-making• Covers the formal and computational methods for characterizing and addressing problems• Provides a critical assessment of their intellectual foundations and practical utility• Features rich pedagogy and extensive instructor resources

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers