S. Barocas, M. Hardt and A. Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023

Leonardo De Pin

doi:10.54103/1757-0522/29851

February 23, 2026The reasonerOpen Access

S. Barocas, M. Hardt and A. Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023

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Key Points

The review aims to explore the concept of fairness in machine learning and its implications across various fields.
Comprehensive review of existing literature on fairness in machine learning.
Interdisciplinary approach incorporating ethics, law, and social sciences.
Analysis of the accessibility and open access nature of the book.
Highlights the limitations and opportunities related to fairness in algorithmic decision-making.
Emphasizes the need for an interdisciplinary understanding of fairness.
Discusses the contribution of social sciences to the topic of machine learning fairness.

Implication

This review highlights fairness in algorithmic decision-making, suggesting interdisciplinary insights from ethics and law.

Abstract

This review discusses Fairness and Machine Learning by Barocas, Hardt, and Narayanan, highlighting itscomprehensive and interdisciplinary treatment of fairness in algorithmic decision-making. The book isaccessible, open access, and enriched by insights from ethics, law, and the social sciences.

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Cite This Study

Leonardo De Pin (2026) studied this question.

synapsesocial.com/papers/699fe28895ddcd3a253e64ee https://doi.org/10.54103/1757-0522/29851

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S. Barocas, M. Hardt and A. Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023

Key Points

Implication

Abstract

Discussion

Cite This Study

S. Barocas, M. Hardt and A. Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023

Key Points

Implication

Abstract

Discussion

Cite This Study

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