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Fairness and Machine Learning

- Limitations and Opportunities
Af: Moritz Hardt, Solon Barocas Engelsk Hardback

Fairness and Machine Learning

- Limitations and Opportunities
Af: Moritz Hardt, Solon Barocas Engelsk Hardback
Tjek vores konkurrenters priser
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
Tjek vores konkurrenters priser
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Tjek vores konkurrenters priser
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
Produktdetaljer
Sprog: Engelsk
Sider: 320
ISBN-13: 9780262048613
Indbinding: Hardback
Udgave:
ISBN-10: 0262048612
Udg. Dato: 19 dec 2023
Længde: 30mm
Bredde: 237mm
Højde: 184mm
Forlag: MIT Press Ltd
Oplagsdato: 19 dec 2023
Forfatter(e): Moritz Hardt, Solon Barocas
Forfatter(e) Moritz Hardt, Solon Barocas


Kategori Informationsteknologi: generelt


ISBN-13 9780262048613


Sprog Engelsk


Indbinding Hardback


Sider 320


Udgave


Længde 30mm


Bredde 237mm


Højde 184mm


Udg. Dato 19 dec 2023


Oplagsdato 19 dec 2023


Forlag MIT Press Ltd

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