November 18 2021

Machine Learning for High-Risk Applications


Machine Learning for High-Risk Applications


Machine Learning for High-Risk Applications
pdf, epub | 12.43 MB | English | Isbn:‎ B079KLDW21 | Author: Patrick Hall | Year: 2018



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. 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



  • Category:Pattern Recognition, Machine Theory, Computer Vision & Pattern Recognition


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