Enabling AI Adoption through Assurance

Autores/as

  • 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

Palabras clave:

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

Resumen

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.

Descargas

Publicado

2022-05-04

Cómo citar

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

Número

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

Sección

Tutorials

Licencia

Derechos de autor 2022 Jaganmohan Chandrasekaran, Feras A. Batarseh, Laura Freeman, Raghu Kacker, M S Raunak, Rick Kuhn

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.