Machine Learning for High-Risk Applications

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

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.