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AI, Ethical Issues and Explainability—Applied Biometrics

  • Book
  • © 2022

Overview

Authors:
  1. KC Santosh

    1. University of South Dakota, Vermillion, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Casey Wall

    1. University of South Dakota, Vermillion, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Discusses state-of-the-art AI-guided tools, their explainability and ethical issues
  • Presents a case study which covers both XAI and ethical issues
  • Serves as a reference for researchers and practitioners in academia and industry

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. AI and Ethical Issues

    • KC Santosh, Casey Wall
    Pages 1-20

  3. EXplainable AI

    • KC Santosh, Casey Wall
    Pages 21-28

  4. Trustworthy and EXplainable AI for Biometrics

    • KC Santosh, Casey Wall
    Pages 29-46

  5. Illicit Economy, Threats, and Biometrics

    • KC Santosh, Casey Wall
    Pages 47-52

  6. AI, Ethical Issues, and Explainability for Biometrics—Summary and What Else?

    • KC Santosh, Casey Wall
    Pages 53-55
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Keywords

About this book

AI has contributed a lot and biometrics is no exception. To make AI solutions commercialized/fully functional, one requires trustworthy and explainable AI (XAI) solutions while respecting ethical issues. Within the scope of biometrics, the book aims at both revisiting ethical AI principles by taking into account state-of-the-art AI-guided tools and their responsibilities i.e., responsible AI. With this, the long-term goal is to connect with how we can enhance research communities that effectively integrate computational expertise (with both explainability and ethical issues). It helps combat complex and elusive global security challenges that address our national concern in understanding and disrupting the illicit economy.

Authors and Affiliations

  • University of South Dakota, Vermillion, USA

    KC Santosh, Casey Wall

About the authors

Professor KC Santosh, Ph.D. is the Chair of the Department of Computer Science (CS) at the University of South Dakota (USD). Prior to that, he worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research center, Université de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research center (France), where he received his PhD in Computer Science - Artificial Intelligence.  His research projects, primarily in Applied AI, are funded (of more than $2m) by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He completed leadership and training programs for Deans/Chairs (organized by the Councils of Colleges of Arts & Sciences (U.S. 21)) and PELI - President's Executive Leadership Institute (USD 21). He is highly motivated/interested in academic leadership. To name a few, Prof. Santosh is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD 2021), the President's Research Excellence Award (USD 2019) and the Ignite Award from the U.S. Department of Health & Human Services (HHS 2014).

 

Casey Wall is a prestigious National Science Foundation (NSF) Research Traineeship Program awardee—graduate student at University of South Dakota. His research interests lie in Multimodal representation and learning for security and counterfeiting that addresses the national concern in understanding and disrupting the illicit economy.


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