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

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.