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Evaluating Trustworthiness of AI Systems

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Manage episode 376935557 series 1264075
Content provided by Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

AI system trustworthiness is dependent on end users’ confidence in the system’s ability to augment their needs. This confidence is gained through evidence of the system’s capabilities. Trustworthy systems are designed with an understanding of the context of use and careful attention to end-user needs. In this webcast, SEI researchers discuss how to evaluate trustworthiness of AI systems given their dynamic nature and the challenges of managing ongoing responsibility for maintaining trustworthiness.

What attendees will learn:

  • Basic understanding of what makes AI systems trustworthy
  • How to evaluate system outputs and confidence
  • How to evaluate trustworthiness to end users (and affected people/communities)
  continue reading

151 episodes

Artwork
iconShare
 
Manage episode 376935557 series 1264075
Content provided by Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

AI system trustworthiness is dependent on end users’ confidence in the system’s ability to augment their needs. This confidence is gained through evidence of the system’s capabilities. Trustworthy systems are designed with an understanding of the context of use and careful attention to end-user needs. In this webcast, SEI researchers discuss how to evaluate trustworthiness of AI systems given their dynamic nature and the challenges of managing ongoing responsibility for maintaining trustworthiness.

What attendees will learn:

  • Basic understanding of what makes AI systems trustworthy
  • How to evaluate system outputs and confidence
  • How to evaluate trustworthiness to end users (and affected people/communities)
  continue reading

151 episodes

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