Machine Learning for High-Risk Applications
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A01=James Curtis
A01=Parul Pandey
A01=Patrick Hall
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AI deep learning Machine Learning Artificial Intelligence Gradient Boosting XGBoost Fairness accountability and transparency in machine learning (FATML) Algorithmic Discrimination Explainable Artificial Intelligence (XAI) Information Security Data Privacy
Author_James Curtis
Author_Parul Pandey
Author_Patrick Hall
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COP=United States
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Language_English
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Product details
- ISBN 9781098102432
- Dimensions: 178 x 232mm
- Publication Date: 02 May 2023
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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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 practice
Get a guide to existing standards, laws, and assessments for adopting AI technologies
Look at how existing roles at companies are evolving to incorporate responsible AI
Examine business best practices and recommendations for implementing responsible AI
Learn technical approaches for responsible AI at all stages of system development
Patrick Hall is principal scientist at bnh.ai, a Cc.C.-based law firm focused on AI and data analytics, and visiting faculty at the George Washington University School of Business (GWSB). James Curtis is a quantitative researcher focused on US power markets and renewable resource asset management. Parul Pandey is a Machine Learning Engineer at Weights & Biases.
