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Algorithmic fairness as a book category covers the study and practice of preventing, measuring, and redressing bias and discrimination in automated decision systems. It spans technical methods (fairness-aware machine learning, bias detection, causal analysis, interpretability and debiasing algorithms), normative frameworks (ethical theories, human rights, value-sensitive design), legal and policy dimensions (regulation, accountability, auditing, governance), and applied case studies in domains such as hiring, lending, healthcare, and criminal justice. Books in this category may be aimed at researchers and practitioners seeking algorithms and evaluation metrics, policymakers and lawyers grappling with regulation, or general readers exploring the social impacts of automation, emphasizing interdisciplinary approaches that combine computer science, statistics, social science, and ethics.

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