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 ...
Read More
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
Read Less
Add this copy of Machine Learning for High-Risk Applications: Approaches to cart. $54.63, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2023 by O'Reilly Media.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Machine Learning for High-Risk Applications: Approaches to cart. $54.64, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2023 by O'Reilly Media.
Add this copy of Machine Learning for High-Risk Applications: Approaches to cart. $64.56, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2023 by O'Reilly Media.
Add this copy of Machine Learning for High-Risk Applications: Approaches to cart. $71.70, new condition, Sold by Ria Christie Books rated 4.0 out of 5 stars, ships from Uxbridge, MIDDLESEX, UNITED KINGDOM, published 2023 by O'Reilly Media.
Add this copy of Machine Learning for High-Risk Applications to cart. $72.36, new condition, Sold by Paperbackshop rated 4.0 out of 5 stars, ships from Bensenville, IL, UNITED STATES, published 2023 by O'Reilly.
Add this copy of Machine Learning for High-Risk Applications: Approaches to cart. $75.46, new condition, Sold by Booksplease rated 4.0 out of 5 stars, ships from Southport, MERSEYSIDE, UNITED KINGDOM, published 2023 by O'Reilly Media.
