Machine Learning for High-Risk Applications (Paperback)
Idioma: inglés
Editorial: O'Reilly Media, Sebastopol, 2023
- Tapa blanda
- Nuevo
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Vendedor de AbeBooks desde 22 de junio de 2007
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EUR 76,77
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Paperback. 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 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …
N° de ref. del artículo 9781098102432
- Título
- Machine Learning for High-Risk Applications (Paperback)
- Autor
- Patrick Hall
- Editorial
- O'Reilly Media, Sebastopol
- Año de publicación
- 2023
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1098102436
- ISBN 13
- 9781098102432
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.
This book describes approaches to responsible AI--a holistic framework 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. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
- Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
- Learn how to create a successful and impactful AI risk management practice
- Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
- Engage with interactive resources on GitHub and Colab
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Acerca del autor
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.
James Curtis is a quantitative researcher at Solea Energy, where he is focused on using statistical forecasting to further the decarbonization of the US power grid. He previously served as a consultant for financial services organizations, insurers, regulators, and health care providers to help build more equitable AI/ML models. James holds an MS in Mathematics from the Colorado School of Mines.
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin's Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
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