Machine Learning for High-Risk Applications (Second Release) PDF
702 Pages·2021·7.1805 MB·other
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Description:
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. Authors Patrick Hall and Rumman Chowdhury 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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Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.
