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Indhold leveret af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
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Evaluating Trustworthiness of AI Systems

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Manage episode 376935557 series 1264075
Indhold leveret af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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

174 episoder

Artwork
iconDel
 
Manage episode 376935557 series 1264075
Indhold leveret af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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

174 episoder

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