Evaluating Trustworthiness of AI Systems

Software Engineering Institute (SEI) Webcast Series

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MP4•בית הפרקים

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)

166 פרקים

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

Evaluating Trustworthiness of AI Systems

Software Engineering Institute (SEI) Webcast Series

63 subscribers

published לפני שנתיים

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MP4•בית הפרקים

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)

166 פרקים

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Software Engineering Institute (SEI) Webcast Series

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Classic tool suites that are used to process network flow records deal with very limited detail on the network connections they summarize. These tools limit detail for several reasons: (1) to maintain long-baseline data, (2) to focus on security-indicative data fields, and (3) to support data collection across large or complex infrastructures. However, a consequence of this limited detail is that analysis results based on this data provide information about indications of behavior rather than information that accurately identifies behavior with high confidence. In this webcast, Tim Shimeall and Katherine Prevost discuss how to use IPFIX-formatted data with detail derived from deep packet inspection (DPI) to provide increased confidence in identifying behavior.…

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Evaluating Trustworthiness of AI Systems