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Author: Aileen Nielsen
Publisher: O'Reilly Media
ISBN: 1492075701
Category : Computers
Languages : en
Pages : 346
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Book Description
Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.
Author: Aileen Nielsen
Publisher: O'Reilly Media
ISBN: 1492075701
Category : Computers
Languages : en
Pages : 346
Get Book Here
Book Description
Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.
Author: Aileen Nielsen
Publisher: O'Reilly Media
ISBN: 9781492075738
Category :
Languages : en
Pages : 175
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Book Description
Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.
Author: Haukur Logi Karlsson
Publisher: Bloomsbury Publishing
ISBN: 1509935436
Category : Law
Languages : en
Pages : 192
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Book Description
What constitutes a fair procedure when it comes to EU competition law? This innovative book seeks to understand the philosophical considerations at the core of conflicting procedural fairness arguments in EU competition law practice. The author argues for a conceptualisation of procedural fairness as a distributional issue that can be solved by a practical fairness theory and a comprehensive methodology. To illustrate the usefulness of the conceptualisation, three procedural fairness problems from recent EU competition law practice are analysed: - the KME–Chalkor cases; - the Groupe Gascogne cases; - the regulatory question about using a collective redress mechanism for private enforcement of EU competition law. This unique approach provides a robust philosophical and methodological foundation for arguing about a wide range of procedural fairness dilemmas. The book is a must-read for academics and practitioners seeking an imaginative perspective on the philosophical foundations of arguments about procedural fairness in EU competition law and beyond.
Author: Ludovico Boratto
Publisher: Springer Nature
ISBN: 3031372492
Category : Computers
Languages : en
Pages : 187
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Book Description
This book constitutes the refereed proceedings of the 4th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held in Dublin, Ireland, in April 2023. The 10 full papers and 4 short papers included in this book were carefully reviewed and selected from 36 submissions. The present recent research in the following topics: biases exploration and assessment; mitigation strategies against biases; biases in newly emerging domains of application, including healthcare, Wikipedia, and news, novel perspectives; and conceptualizations of biases in the context of generative models and graph neural networks.
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Category : Public utilities
Languages : en
Pages : 284
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Book Description
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Category : American periodicals
Languages : en
Pages : 854
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Book Description
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Category :
Languages : en
Pages : 460
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Book Description
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Category : Criminal law
Languages : en
Pages : 808
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Book Description
Author: California (State).
Publisher:
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Category : Law
Languages : en
Pages : 238
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Book Description
Number of Exhibits: 7
Author: John McLevey
Publisher: SAGE
ISBN: 1529737591
Category : Social Science
Languages : en
Pages : 556
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Book Description
Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.