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batarseh feras a. (curatore); freeman laura (curatore) - ai assurance

AI Assurance Towards Trustworthy, Explainable, Safe, and Ethical AI


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Lingua: Inglese
Pubblicazione: 10/2022

Note Editore

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


1. An introduction to AI assurance
2. Setting the goals for ethical, unbiased and fair AI
3. An overview of explainable and interpretable AI
4. Bias, Fairness, and assurance in AI: Overview and Synthesis
5. An evaluation of the potential global impacts of AI assurance
6. The role of inference in AI: start S.M.A.L.L. with muindful models
7. Outlier detection using AI: a survey
8. AI assurance using casual inference: application to public policy
9. Data collection, wrangling and preprocessing for AI assurance
10. Coordination-aware assurance for end-to-end machine learning systems: the R3E approach
11. Assuring AI methods for economic policymaking
12. Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare
13. Recent advances in uncertainty quantification methods for engineering problems
14. Socially responsible AI assurance in precision agriculture for farmers and policymakers
15. The application of AI assurance in precision farming and agricultural economics
16. Bringing dark data to light with AI for evidence-based policy making


Feras A. Batarseh is a Teaching Assistant Professor with the Data Analytics Program at Georgetown University, Washington, D.C., and a Research Assistant Professor with the College of Science at George Mason University (GMU), Fairfax, VA. His research and teaching span the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his PhD and MSc in Computer Engineering from the University of Central Florida (UCF) (2007, 2011) and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published and edited several book chapters. He is the author and editor of Federal Data Science , another book by Elsevier's Academic Press. Dr. Batarseh has taught data science and software engineering courses at multiple universities including Georgetown, GMU, UCF, The University of Maryland, Baltimore County (UMBC), as well as George Washington University (GWU).
Dr. Laura Freeman is a Research Associate Professor at the Department of Statistics and the Director of the Intelligent Systems Lab at Virginia Tech's Hume Center. Her research leverages experimental methods for conducting research that brings together cyber-physical systems, Data Science, Artificial Intelligence, and Machine Learning to address critical challenges in national security. She is a CCI fellow.

Altre Informazioni



Condizione: Nuovo
Dimensioni: 235 x 191 mm
Formato: Brossura
Pagine Arabe: 448

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