Machine Learning for High–Risk Applications: Techniques for Responsible AI
Autor Patrick Hall, James Curtis, Parul Pandeyen Limba Engleză Paperback – mai 2023
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Specificații
ISBN-10: 1098102436
Pagini: 400
Dimensiuni: 177 x 233 x 30 mm
Greutate: 0.82 kg
Ediția:1
Editura: O'Reilly
Descriere
The past decade has witnessed a wide adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.
This book describes responsible AI, a holistic approach for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. It's an ambitious undertaking that requires a diverse set of talents, experiences, and perspectives. Data scientists and nontechnical oversight folks alike need to be recruited and empowered to audit and evaluate high-impact AI/ML systems.
Author Patrick Hall created this guide for a new generation of auditors and assessors who want to make AI systems better for organizations, consumers, and the public at large. Learn how to create a successful and impactful responsible AI practiceGet a guide to existing standards, laws, and assessments for adopting AI technologiesLook at how existing roles at companies are evolving to incorporate responsible AIExamine business best practices and recommendations for implementing responsible AILearn technical approaches for responsible AI at all stages of system development
Notă biografică
Patrick Hall is principal scientist at BNH.AI, where he advises Fortune 500 companies and cutting-edge startups on AI risk and conducts research in support of NIST's AI risk management framework. He also serves as visiting faculty in the Department of Decision Sciences at The George Washington School of Business, teaching data ethics, business analytics, and machine learning classes.
Before cofounding BNH, Patrick led H2O.ai's efforts in responsible AI, resulting in one of the world's first commercial applications for explainability and bias mitigation in machine learning. He also held global customer-facing roles and R&D research roles at SAS Institute. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Patrick has been invited to speak on topics relating to explainable AI at the National Academies of Science, Engineering, and Medicine, ACM SIG-KDD, and the Joint Statistical Meetings. He has contributed written pieces to outlets like McKinsey.com, O'Reilly Radar, and Thompson Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch, and others.