Keeping Your AI Under Control: A Pragmatic Guide to Identifying, Evaluating, and Quantifying Risks

  • 2h 19m
  • Anand Tamboli
  • Apress
  • 2019

Much of our daily lives intertwine with artificial intelligence. From watching movies recommended by our entertainment streaming service, to interacting with customer service chatbots, to autotagging photos of friends in our social media apps, AI plays an invisible part in enriching our lives. While AI may be seen as a panacea for enterprise advancement and consumer convenience, it is still an emerging technology, and its explosive growth needs to be approached with proper care and preparation. How do we tackle the challenges it presents, and how do we make sure that it does precisely what it is supposed to do?

In Keeping Your AI Under Control, author Anand Tamboli explores the inherent risk factors of the widespread implementation of artificial intelligence. The author delves into several real-life case studies of AI gone wrong, including Microsoft’s 2016 chatbot disaster, Uber’s autonomous vehicle fatally wounding a pedestrian, and an entire smart home in Germany dangerously malfunctioning because of one bad lightbulb. He expertly addresses the need to challenge our current assumptions about the infallibility of technology.

The importance of data governance, rigorous testing before roll-out, a chain of human accountability, ethics, and much more are all detailed in Keeping Your AI Under Control. Artificial intelligence will not solve all of our problems for good, but it can (and will) present us with new solutions. These solutions can only be achieved with proper planning, continued maintenance, and above all, a foundation of attuned human supervision.

In this Book

  • Artificial Intelligence Beyond 2020
  • Learning Lessons from Past Fiascoes
  • Understanding AI Risks and Its Impacts
  • Evaluating Risks of the AI Solution
  • De-risking AI Solution Deployment
  • Good AI in the Hands of Bad Users
  • A Systematic Approach to Risk Mitigation
  • Teach Meticulously and Test Rigorously
  • AI Supervision with a Red Team
  • Handling Residual Risks
  • When Working with Emerging Technologies
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