{"product_id":"9783032337504","title":"Fairness in Machine Learning From Theory to Regulation","description":"\u003ch3\u003eSpringerBriefs in Intelligent Systems\u003c\/h3\u003e\u003ch1\u003eFairness in Machine Learning\u003c\/h1\u003e\u003ch2\u003eFrom Theory to Regulation\u003c\/h2\u003e\u003ch3\u003eCharlotte Laclau | Antoine Gourru | Winston Maxwell\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eFairness in machine learning is an increasingly critical topic as AI-driven systems become more embedded in decision-making processes that impact individuals and society. Research on fairness remains fragmented across disciplines, including social sciences, law, and machine learning, and many existing books either focus on theoretical aspects or provide applied ML techniques without addressing fairness-related pitfalls. This book fills that gap, offering a structured and balanced examination of fairness in ML that covers conceptual definitions, fairness-aware algorithms, trade-offs, open challenges, and the regulatory landscape, with clarity on how fairness is measured and the tensions between different fairness criteria.\u003c\/p\u003e\r\n\u003cp\u003eThe book is structured to first introduce fairness as a concept, outlining its various definitions and disciplinary perspectives, including philosophical, legal, economic, and social viewpoints, as well as causal and counterfactual notions of fairness. It then explores approaches to mitigating bias in ML, from pre-processing techniques that adjust datasets to in-processing and post-processing methods. Special attention is given to the fairness challenges that arise in generative models, an area that has gained prominence with the rise of large language models and text-to-image systems. The book also discusses the European regulatory landscape, including the AI Act and non-discrimination law, and how these legal frameworks relate to technical notions of fairness.\u003c\/p\u003e\r\n\u003cp\u003eReaders will find particular interest in the discussion of fairness trade-offs and theoretical results, such as the price of fairness and incompatibility results between fairness criteria, which highlight the complex decisions practitioners must make when striving for fairness while maintaining model accuracy and interpretability. The book also emphasizes open challenges, such as the lack of diverse annotated datasets, the complexities of intersectionality, and fairness under distribution shift. By reading this book, researchers, practitioners, and policymakers will gain a deeper understanding of fairness in ML, learning not just about existing solutions but also about the nuances and limitations of current approaches, and will be equipped with both theoretical knowledge and practical tools to assess fairness considerations in their ML models.\u003c\/p\u003e\r\n\u003cp\u003eA background in machine learning and statistics is recommended for readers to fully grasp the technical discussions.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cstrong\u003eCharlotte Laclau \u003c\/strong\u003eis an Associate Professor at Télécom Paris (Maître de Conférences, HDR) at Télécom Paris, Institut Polytechnique de Paris, where she leads research at the intersection of machine learning on graphs and fairness in machine learning, using optimal transport as a recurring methodological tool. She currently serves as Principal Investigator of an ANR project on fairness for graph-structured data and contributes to the national PEPR Foundry program on AI robustness and reliability, supervising several PhD students working on these questions. She is affiliated with the Hi! Paris center, the Center on Data Analytics and Artificial Intelligence for Science, Business and Society.\u003c\/p\u003e\r\n\u003cp\u003e\u003cstrong\u003eAntoine Gourru\u003c\/strong\u003e is an Associate Professor (Maître de Conférences) at Télécom Saint-Étienne and a member of the Machine Learning Team at the Hubert Curien Laboratory. His research focuses on responsible machine learning, with an emphasis on fairness, frugality, and explainability in natural language processing and complex data. He contributes to several funded academic projects, including ANR DIKé and ANR FAMOUS, and leads the regional i-démo DavinciDOC project on the laboratory side. He also co-supervises several PhD students and research staff working on fair NLP, frugal LLM systems, and responsible AI, and serves as co-head of the Computer Science Department at Télécom Saint-Étienne.\u003c\/p\u003e\r\n\u003cp\u003e\u003cstrong\u003eWinston Maxwell\u003c\/strong\u003e is Professor Emeritus at Télécom Paris – Institut Polytechnique de Paris, where he writes on subjects related to the regulation of data, AI and telecommunications. He previously had a career in private practice as a partner of the international law firm Hogan Lovells. Winston completed his law degree (JD) at Cornell, his PhD in economics at Télécom Paris, and his HDR (Habilitation à Diriger des Recherches) at the University of Paris Panthéon Sorbonne. His research focuses on the regulation of AI, and in particular human control over algorithmic systems, explainability and bias. Winston co-founded the “Operational AI Ethics” program at Telecom Paris, which includes AI Ethics teaching at Institut Polytechnique de Paris. Winston is a member of the French Academy of Technologies.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e22 October 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032337504\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003ePaperback \/ softback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":51031522050188,"sku":"9783032337504","price":49.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032337504.jpg?v=1782482628","url":"https:\/\/lateknightbooks.com\/products\/9783032337504","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}