Limitations Of Fairness In 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
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.
Artificial Intelligence And Machine Learning
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Author : Frans A. Oliehoek
language : en
Publisher: Springer Nature
Release Date : 2024-11-28
Artificial Intelligence And Machine Learning written by Frans A. Oliehoek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Computers categories.
This book constitutes the refereed proceedings of the 35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023, held in Delft, The Netherlands, during November 8–10, 2023. The 14 papers included in these proceedings were carefully reviewed and selected from 47 submissions. These papers focus on various aspects of Artificial Intelligence and Machine learning, including Natural Language Processing and Reinforcement Learning, and their applications.
Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications
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Author : Jayesh Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-10
Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications written by Jayesh Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Computers categories.
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.
Advances In Bias And Fairness In Information Retrieval
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Author : Ludovico Boratto
language : en
Publisher: Springer Nature
Release Date : 2022-06-18
Advances In Bias And Fairness In Information Retrieval written by Ludovico Boratto and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-18 with Computers categories.
This book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022. The 9 full papers and 4 short papers were carefully reviewed and selected from 34 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web.
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.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Machine Learning And Knowledge Discovery In Databases Research Track
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Author : Albert Bifet
language : en
Publisher: Springer Nature
Release Date : 2024-08-30
Machine Learning And Knowledge Discovery In Databases Research Track written by Albert Bifet and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. The papers presented in these proceedings are from the following three conference tracks: - Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII. Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII. Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.
Hci International 2024 Late Breaking Papers
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Author : Vincent G. Duffy
language : en
Publisher: Springer Nature
Release Date : 2024-12-03
Hci International 2024 Late Breaking Papers written by Vincent G. Duffy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
This nine-volume set LNCS 15473-15482 constitutes the proceedings of the 26th International Conference, HCI International 2023, in Washington, DC, USA, in June/July 2024. For the HCCII 2024 proceedings, a total of 1271 papers and 309 posters was carefully reviewed and selected from 5108 submissions. Additionally, 222 papers and 104 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Theories, Methods and Tools; Multimodal Interaction; Interacting with Chatbots and Generative AI; Interacting in Social Media; Fintech, Consumer Behavior and the Business Environment; Design for Health and Wellbeing; Ergonomics and Digital Human Modelling; Virtual Experiences in XR and the Metaverse; Playing Experiences; Design for Learning; New Cultural and Tourism Experiences; Accessibility and Design for All; Design for Older Adults; User Experience Design and Evaluation: Novel Approaches and Case Studies; Safety, Security and Privacy; HCI in Automated Vehicles and Automotive; HCI in Aviation, Transport and Safety; Human-Centered AI; AI for Decision Making and Sentiment Analysis.
Ethics Of Data And Analytics
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Author : Kirsten Martin
language : en
Publisher: CRC Press
Release Date : 2022-05-12
Ethics Of Data And Analytics written by Kirsten Martin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-12 with Business & Economics categories.
The ethics of data and analytics, in many ways, is no different than any endeavor to find the "right" answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better. Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. The text takes a more skeptical, ironic approach to current answers and concepts while identifying and having solidarity with others. Applying this to the endeavor to understand the ethics of data and analytics, the text emphasizes finding multiple ethical approaches as ways to engage with current problems to find better solutions rather than prioritizing one set of concepts or theories. The book works through cases to understand those marginalized by data analytics programs as well as those empowered by them. Three themes run throughout the book. First, data analytics programs are value-laden in that technologies create moral consequences, reinforce or undercut ethical principles, and enable or diminish rights and dignity. This places an additional focus on the role of developers in their incorporation of values in the design of data analytics programs. Second, design is critical. In the majority of the cases examined, the purpose is to improve the design and development of data analytics programs. Third, data analytics, artificial intelligence, and machine learning are about power. The discussion of power—who has it, who gets to keep it, and who is marginalized—weaves throughout the chapters, theories, and cases. In discussing ethical frameworks, the text focuses on critical theories that question power structures and default assumptions and seek to emancipate the marginalized.