Feras A. Batarseh & Laura Freeman 
AI Assurance 
Towards Trustworthy, Explainable, Safe, and Ethical AI

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