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.