AI Assurance
Towards Trustworthy, Explainable, Safe, and Ethical AI
- 1st Edition - October 12, 2022
- Imprint: Academic Press
- Editors: Feras A. Batarseh, Laura Freeman
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 9 1 9 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 8 8 2 - 4
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 A… Read more
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Request a sales quoteAI 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
Scientists, researchers, and MSc. PhD. students from the fields of Computer Science and Engineering. The audience includes researchers, practitioners, and students in the fields of computer architecture and operating systems, as well as management information systems
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- Authors affiliations
- A note by the editors
- A note on the book cover
- Foreword 1
- Foreword 2
- Foreword 3
- Part 1: Foundations of AI assurance
- 1: An introduction to AI assurance
- Graphical abstract
- Abstract
- 1.1. Motivation and overview
- 1.2. The need for new assurance methods
- 1.3. Conclusion
- References
- 2: Setting the goals for ethical, unbiased, and fair AI
- Graphical abstract
- Abstract
- 2.1. Introduction and background
- 2.2. Ethical AI but… how?
- 2.3. Conclusion
- References
- 3: An overview of explainable and interpretable AI
- Graphical abstract
- Abstract
- Acknowledgements
- 3.1. Introduction
- 3.2. Methods and materials
- 3.3. Experiments using XAI models
- 3.4. Discussion
- 3.5. Future work
- 3.6. Conclusion
- References
- 4: Bias, fairness, and assurance in AI: overview and synthesis
- Graphical abstract
- Abstract
- 4.1. Introduction
- 4.2. Assurance and ethical AI
- 4.3. Validation methods
- 4.4. Synthesis of the literature
- 4.5. Conclusion
- References
- 5: An evaluation of the potential global impacts of AI assurance
- Graphical abstract
- Abstract
- Acknowledgement
- 5.1. Introduction
- 5.2. Literature review
- 5.3. Methodology & modeling
- 5.4. Results and analysis
- 5.5. Conclusion
- References
- Part 2: AI assurance methods
- 6: The role of inference in AI: Start S.M.A.L.L. with mindful modeling
- Graphical abstract
- Abstract
- Acknowledgements
- 6.1. Real wisdom on artificial intelligence
- 6.2. Fundamentals: decision-making, heuristics and cognitive biases
- 6.3. Fundamentals: yearning to make sense of the world through models and inference
- 6.4. Bolstering AI assurance: reducing biases with inferential methods
- 6.5. Rest assured: mindful approaches in modeling may help avoid another AI winter
- 6.6. Further reading
- References
- 7: Outlier detection using AI: a survey
- Graphical abstract
- Abstract
- 7.1. Introduction and motivation
- 7.2. Outlier detection methods
- 7.3. Tools for outlier detection
- 7.4. Datasets for outlier detection
- 7.5. AI assurance and outlier detection
- 7.6. Conclusions
- References
- 8: AI assurance using causal inference: application to public policy
- Graphical abstract
- Abstract
- Acknowledgements
- 8.1. Introduction and motivation
- 8.2. Causal inference
- 8.3. AI assurance using causal inference
- 8.4. Network representations of data
- 8.5. Conclusion
- References
- 9: Data collection, wrangling, and pre-processing for AI assurance
- Graphical abstract
- Abstract
- 9.1. Introduction and motivation
- 9.2. Relevant data characteristics
- 9.3. Data pre-processing: data wrangling and munging
- 9.4. Data processing architectures: ETL & ELT
- 9.5. DataOps: data operations automation management
- 9.6. Data tagging, provenance, and lineage
- References
- 10: Coordination-aware assurance for end-to-end machine learning systems: the R3E approach
- Graphical abstract
- Abstract
- Acknowledgements
- 10.1. Introduction
- 10.2. Background and motivation
- 10.3. Key elements of R3E approach
- 10.4. Illustrative examples
- 10.5. Discussion
- 10.6. Conclusions and future work
- References
- Part 3: AI assurance and applications
- 11: Assuring AI methods for economic policymaking
- Graphical abstract
- Abstract
- Acknowledgements
- 11.1. Introduction to harnessing AI for economics
- 11.2. Commonplace explainability methods
- 11.3. Mitigating bias in AI models for economic prediction
- 11.4. Conclusion
- References
- 12: Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare
- Graphical abstract
- Abstract
- 12.1. Introduction
- 12.2. Ontological perspectives
- 12.3. Ethics frameworks
- 12.4. Governance in the healthcare domain
- 12.5. Societal disparities in wellbeing
- 12.6. Conclusion
- References
- 13: Recent advances in uncertainty quantification methods for engineering problems
- Graphical abstract
- Abstract
- Acknowledgements
- 13.1. Introduction
- 13.2. Polynomial chaos method for UQ
- 13.3. Gaussian Process or Kriging for UQ
- 13.4. Polynomial chaos Kriging for UQ
- 13.5. Uncertainty quantification of a supersonic nozzle
- 13.6. Conclusions
- References
- 14: Socially responsible AI assurance in precision agriculture for farmers and policymakers
- Graphical abstract
- Abstract
- 14.1. Introduction
- 14.2. Current methods of AI assurance in agriculture
- 14.3. Agricultural policy
- 14.4. AI assurance in agriculture recommendations
- 14.5. Conclusion
- CRediT authorship contribution statement
- References
- 15: The application of artificial intelligence assurance in precision farming and agricultural economics
- Graphical abstract
- Abstract
- Acknowledgements
- 15.1. Introduction
- 15.2. AI for smart farms
- 15.3. Insight into data driven farming
- 15.4. Larger policy implications
- 15.5. Conclusion
- References
- 16: Bringing dark data to light with AI for evidence-based policymaking
- Graphical abstract
- Abstract
- 16.1. Introduction
- 16.2. The dataset for AIM
- 16.3. Feature creation
- 16.4. Learning the trends
- 16.5. Discussions and future directions
- 16.6. Ethics of AI in public policy
- References
- Index
- Edition: 1
- Published: October 12, 2022
- Imprint: Academic Press
- No. of pages: 600
- Language: English
- Paperback ISBN: 9780323919197
- eBook ISBN: 9780323918824
FB
Feras A. Batarseh
Feras A. Batarseh is an Associate Professor with the Department of Biological Systems Engineering at Virginia Tech (VT) and the Director of A3 (AI Assurance and Applications) Lab. His research spans the areas of AI Assurance, Cyberbiosecurity, AI for Agriculture and Water, and Data-Driven Public Policy. His work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published multiple chapters and books, his two recent books are: "Federal Data Science", and "Data Democracy", both by Elsevier’s Academic Press.
Dr. Batarseh is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), the Agricultural and Applied Economical Association (AAEA), and the Association for the Advancement of Artificial Intelligence (AAAI). He has taught AI and Data Science courses at multiple universities including George Mason University (GMU), University of Maryland - Baltimore County (UMBC), Georgetown University, and George Washington University (GWU).
Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), a Juris Masters of Law from GMU (2022), and a Graduate Certificate in Project Leadership from Cornell University (2016). He currently holds courtesy appointments with the Center for Advanced Innovation in Agriculture (CAIA), National Security Institute (NSI), and the Department of Electrical and Computer Engineering at VT.
Affiliations and expertise
Associate Professor, Department of Biological Systems Engineering at Virginia Tech (VT) USALF
Laura Freeman
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.
Affiliations and expertise
Research Associate Professor, Department of Statistics and Director of the Intelligent Systems Lab, Virginia Tech University, Blacksburg, VA, USARead AI Assurance on ScienceDirect