Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.
Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.
• Identify potential bias and discrimination in data science models
• Use preventive measures to minimize bias when developing data modeling pipelines
• Understand what data pipeline components implicate security and privacy concerns
• Write data processing and modeling code that implements best practices for fairness
• Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models
• Apply normative and legal concepts relevant to evaluating the fairness of machine learning models

lgrsnf/1492075736.epub

zlib/Computers/Aileen Nielsen/Practical Fairness_16352058.epub

Nielsen, Aileen

O'Reilly Media, [Place of publication not identified], 2020

Business book summary, First edition, Sebastopol, CA, 2020

United States, United States of America

1st edition, Sebastopol, CA, 2021

1, PS, 2021

lg2871450

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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.

2020-12-01