Enabling AI Adoption through Assurance

Authors

  • Jaganmohan Chandrasekaran Virginia Tech
  • Feras A. Batarseh Virginia Tech
  • Laura Freeman Virginia Tech
  • Raghu Kacker NIST
  • M S Raunak NIST
  • Rick Kuhn NIST

DOI:

https://doi.org/10.32473/flairs.v35i.130726

Keywords:

AI Assurance, Trustworthy AI, AI for Public Policy, AI Standards

Abstract

With the broader adoption of AI-enabled software systems, it is necessary to provide assurance to the layman user that the AI system will behave as intended. This interactive tutorial will provide an overview of AI assurance, introduce a new set of assurance goals for AI systems, discuss the open challenges in AI assurance, and present recommendations to overcome its drawbacks.

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Published

04-05-2022

How to Cite

Chandrasekaran, J., Batarseh, F. A., Freeman, L., Kacker, R., Raunak, M. S., & Kuhn, R. (2022). Enabling AI Adoption through Assurance. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130726

Issue

Vol. 35 (2022): Proceedings of FLAIRS-35

Section

Tutorials

License

Copyright (c) 2022 Jaganmohan Chandrasekaran, Feras A. Batarseh, Laura Freeman, Raghu Kacker, M S Raunak, Rick Kuhn

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.