AI Assurance

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Download or read online AI Assurance PDF book by Feras A. Batarseh in ePUB, PDF or Kindle eBooks. Published by Academic Press in 28 October 2022 the book become immediate popular and critical acclaim in Science books with total hardcover page 602. Click Download Book button to get book file. Read some info about this book below.

  • Author : Feras A. Batarseh
  • Publisher : Academic Press
  • Release : 28 October 2022
  • ISBN : 9780323918824
  • Page : 602 pages
  • Language : English
  • Total View : 5941 Views
  • File Size : 40,7 Mb

AI Assurance Book PDF summary

AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI provides readers with solutions and a foundational understanding of the methods that can be applied to test AI systems and provide assurance. Anyone developing software systems with intelligence, building learning algorithms, or deploying AI to a domain-specific problem (such as allocating cyber breaches, analyzing causation at a smart farm, reducing readmissions at a hospital, ensuring soldiers’ safety in the battlefield, or predicting exports of one country to another) will benefit from the methods presented in this book. As AI assurance is now a major piece in AI and engineering research, this book will serve as a guide for researchers, scientists and students in their studies and experimentation. Moreover, as AI is being increasingly discussed and utilized at government and policymaking venues, the assurance of AI systems—as presented in this book—is at the nexus of such debates. Provides readers with an in-depth understanding of how to develop and apply Artificial Intelligence in a valid, explainable, fair and ethical manner Includes various AI methods, including Deep Learning, Machine Learning, Reinforcement Learning, Computer Vision, Agent-Based Systems, Natural Language Processing, Text Mining, Predictive Analytics, Prescriptive Analytics, Knowledge-Based Systems, and Evolutionary Algorithms Presents techniques for efficient and secure development of intelligent systems in a variety of domains, such as healthcare, cybersecurity, government, energy, education, and more Covers complete example datasets that are associated with the methods and algorithms developed in the book

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