Isbn: 9780262048613 - Fairness and Machine Learning: Limitations and Opportunities (adaptive Computation and Machine Learning) (33 results)

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Hardcover. Condition: new. Hardcover. An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.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 "This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Condition: new. Hardcover. An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.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 "This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Buch. Condition: Neu. Neuware - 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.…

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