Fairness And Machine Learning
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Fairness And Machine Learning
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Author : Solon Barocas
language : en
Publisher: MIT Press
Release Date : 2023-12-19
Fairness And Machine Learning written by Solon Barocas and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Computers categories.
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
The Fair Algorithm Ensuring Fairness In Machine Learning
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Author : S Williams
language : en
Publisher: NFT Publishing
Release Date : 2025-04-15
The Fair Algorithm Ensuring Fairness In Machine Learning written by S Williams and has been published by NFT Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
In an era where artificial intelligence (AI) plays a pivotal role in shaping decisions across industries, ensuring fairness and eliminating bias in machine learning systems has become more critical than ever. This book dives deep into the pressing challenges of algorithmic fairness , exploring how biases emerge in AI systems and offering actionable strategies to mitigate them. From understanding the roots of data bias and model design flaws to implementing cutting-edge debiasing techniques and fairness metrics , this comprehensive guide equips readers with the tools needed to build ethical, transparent, and inclusive AI. Through real-world case studies from sectors like hiring, lending, healthcare, and law enforcement, the book highlights both failures and successes in achieving equitable outcomes. It examines emerging innovations such as explainable AI (XAI) , bias detection platforms , and fairness-aware algorithms that enhance transparency in AI and foster public trust . Readers will also explore the ethical implications of AI , including debates on privacy, discrimination, and the societal impact of deploying biased algorithms in critical decision-making processes. The narrative further delves into the legal and regulatory frameworks governing AI development, emphasizing the importance of accountability, consumer protection, and adherence to universal values. By applying principles like Kantian ethics to AI practices, the book advocates for responsible AI design that prioritizes inclusivity, equity, and long-term benefits for individuals and communities. Whether you're a developer seeking practical methods to integrate fairness metrics into your workflows or a policymaker navigating regulatory gaps , this resource provides invaluable insights into overcoming barriers such as algorithmic opacity , insufficient diversity in datasets, and resistance to accountability. With a focus on blending empirical evidence with universal ideals, the book concludes with a visionary roadmap toward a future where AI systems are not only fair and transparent but also aligned with ethical principles that uphold human dignity and equality. Packed with knowledge on machine learning ethics , societal inequalities in AI , and innovative trends in fair AI tools, this book is essential reading for anyone committed to building trustworthy, equitable, and impactful AI systems.
Ethical Machine Learning And Artificial Intelligence Ai
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Author : Novi Quadrianto
language : en
Publisher: Frontiers Media SA
Release Date : 2021-12-02
Ethical Machine Learning And Artificial Intelligence Ai written by Novi Quadrianto and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with Science categories.
Limitations Of Fairness In Machine Learning
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Author : Michael Lohaus
language : en
Publisher:
Release Date : 2022
Limitations Of Fairness In Machine Learning written by Michael Lohaus and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The issue of socially responsible machine learning has never been more pressing. An entire field of machine learning is dedicated to investigating the societal aspects of automated decision-making systems and providing technical solutions for algorithmic fairness. However, any attempt to improve the fairness of algorithms must be examined under the lens of potential societal harm. In this thesis, we study existing approaches to fair classification and shed light on their various limitations. First, we show that relaxations of fairness constraints used to simplify the learning process of fair models are too coarse, since the final classifier may be distinctly unfair even though the relaxed constraint is satisfied. In response, we propose a new and provably fair method that incorporates the fairness relaxations in a strongly convex formulation. Second, we observe an increased awareness of protected attributes such as race or gender in the last layer of deep neural networks when we regularize them for fair outcomes. Based on this observation, we construct a neural network that explicitly treats input points differently because of protected personal characteristics. With this explicit formulation, we can replicate the predictions of a fair neural network. We argue that both the fair neural network and the explicit formulation demonstrate disparate treatment-a form of discrimination in anti-discrimination laws. Third, we consider fairness properties of the majority vote-a popular ensemble method for aggregating multiple machine learning models to obtain more accurate and robust decisions. We algorithmically investigate worst-case fairness guarantees of the majority vote when it consists of multiple classifiers that are themselves already fair. Under strong independence assumptions on the classifiers, we can guarantee a fair majority vote. Without any assumptions on the classifiers, a fair majority vote cannot be guaranteed in general, but different fairness regimes are possible: on the one hand, using fair classifiers may improve the worst-case fairness guarantees. On the other hand, the majority vote may not be fair at all.
Machine Learning And Knowledge Discovery In Databases Research Track
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Author : Danai Koutra
language : en
Publisher: Springer Nature
Release Date : 2023-09-16
Machine Learning And Knowledge Discovery In Databases Research Track written by Danai Koutra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Ethics In Artificial Intelligence Bias Fairness And Beyond
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Author : Animesh Mukherjee
language : en
Publisher: Springer Nature
Release Date : 2023-12-29
Ethics In Artificial Intelligence Bias Fairness And Beyond written by Animesh Mukherjee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-29 with Computers categories.
This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments – the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in softwaretesting/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
Ai Fairness And Beyond
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Author : Chris Reed
language : en
Publisher: Bloomsbury Publishing
Release Date : 2024-08-08
Ai Fairness And Beyond written by Chris Reed and has been published by Bloomsbury Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-08 with Law categories.
This book proposes a regulatory system for ensuring that AI makes fair decisions. No one wants to be the subject of an unfair decision made by an AI, and fairness is so important to society that we are likely to want to regulate to demand it. But how? This book attempts to answer that question. The aim of regulation must be for an AI's decisions to match the human conception of fairness. To understand what that is, the book proposes a holistic understanding of fairness, which tells us what regulation must try to achieve. However, regulation is not an abstract activity – it regulates how humans behave, and the humans in question are those who develop and use AI for decision-making. Thus the book investigates how those humans are attempting to achieve AI fairness. It finds that there is a serious mismatch between how technologists conceptualise fairness, compared to other humans. How can AI regulation bridge this gap? Traditional models of regulation cannot solve this problem. Fairness is too nuanced, too contextual, and is ultimately a human emotional response. Instead the book proposes to place the responsibility on the AI community to explain and justify their efforts to achieve fairness, basing regulatory and legal responses on how well that explanation deals with the risks that particular AI presents, and whether the AI operates in accordance with the explanation in use. The book concludes by examining how far this regulatory model might be useful for some of the other social problems which AI generates. An original and significant contribution to the literature on AI regulation, this book is a must-read for those working in the areas of law, regulation, and technology.
Machine Learning For Causal Inference
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Author : Sheng Li
language : en
Publisher: Springer Nature
Release Date : 2023-11-25
Machine Learning For Causal Inference written by Sheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.
This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Dynamics Of Disasters
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Author : Ilias S. Kotsireas
language : en
Publisher: Springer Nature
Release Date : 2024-12-23
Dynamics Of Disasters written by Ilias S. Kotsireas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-23 with Mathematics categories.
Based on the “Sixth International Conference on Dynamics of Disasters” (Piraeus, Greece, July 2023), this volume includes contributions from experts who share their latest discoveries on disasters either caused by natural phenomena or human activities. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery and uses for mathematical models to describe disasters and their impacts. This volume includes additional invited manuscripts from other experts and leaders in the field. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies and will engage readers from a wide variety of fields and backgrounds.
Explainable Artificial Intelligence
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Author : Luca Longo
language : en
Publisher: Springer Nature
Release Date : 2023-10-20
Explainable Artificial Intelligence written by Luca Longo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-20 with Computers categories.
This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.