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