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 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

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