Evaluating Trustworthiness of AI Systems

Software Engineering Institute (SEI) Webcast Series

Treść dostarczona przez Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

2y ago 1:02:08

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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)

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#Tech #Cybersecurity #Securecoding #Softwarearchitecture #Softwaredevelopment #Softwareengineering #Carnegie Mellon University Software Engineering Institute #SEI Members of Technical Staff #Video #Podcasting Education