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 pr...

The first comprehensive introduction to benchmarking, the engine behind progress in AIIn machine learning, researchers split their data into training and test sets, let model builders compete on the test set, and call it a benchmark. Statistical tradition prescribed locking test sets in a vault, but machine learning practitioners shared them freely. Benchmarking shouldn’t have worked, but it did, and the machine learning community never figured out the science behi...