Fairness and Machine Learning
Limitations and Opportunities
By Arvind Narayanan, Moritz Hardt, Solon Barocas | Publisher: MIT Press
About the book
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
Editions of Fairness and Machine Learning
- Hardcover
- ISBN 9780262048613
- EBook
- ISBN 9780262376525
- EBook
- ISBN 9780262376532
Read an Excerpt
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
Frequently Asked Questions
What is Fairness and Machine Learning about?
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
What core themes, tropes, or subjects are explored in Fairness and Machine Learning?
Computing and Information Technology > Computer science > Artificial intelligence (AI) > Machine learning
Where can I read a sample of Fairness and Machine Learning?
You can read an official preview of the few pages here https://www.book2look.com/book/9780262048613
Is Fairness and Machine Learning part of a series, and can it be read as a standalone?
Yes it is a part of series Adaptive Computation and Machine Learning series
Who is/are the Author/s of the book Fairness and Machine Learning?
Arvind Narayanan, Moritz Hardt, Solon Barocas
Who is the Publisher of the book Fairness and Machine Learning?
MIT Press
What are the ISBN numbers for the physical and digital editions?
Fairness and Machine Learning is available as hardcover(ISBN 9780262048613) and ebook(ISBN 9780262376525) and ebook(ISBN 9780262376532)
Where can I buy Fairness and Machine Learning online or support local independent bookshops?
You can buy Fairness and Machine Learning from below
Where can I buy Fairness and Machine Learning?