9780262048613 - Fairness and Machine Learning: Limitations and Opportunities (adaptive Computation and Machine Learning) by Barocas, Solon; Hardt, Moritz (35 results)
- Hardcover
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Hardcover. Condition: New. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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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 advance…d 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.
- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Hardcover. Condition: Brand New. 323 pages. 9.25x6.25x1.00 inches. In Stock.
- Hardcover
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- Hardcover
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Language: English
Published by Information technology: general issues, 2023
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New. 2023. Hardcover. . . . . .
- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Published by Information technology: general issues, 2023
- Hardcover
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Condition: New. 2023. Hardcover. . . . . . Books ship from the US and Ireland.
FAIRNESS AND MACHINE LEARNING: LIMITATIONS AND OPPORTUNITIES (ADAPTIVE COMPUTATION AND MACHINE LEARN
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Seller: Libros Feroces, castellgali, B, SpainLibros Feroces
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- Hardcover
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- Hardcover
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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 advance…d 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.
