AI Assurance for the Public - Trust but Verify, Continuously

Phil Laplante, Rick Kuhn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Artificial intelligence (AI) systems are increasingly seen in many public facing applications such as self-driving land vehicles, autonomous aircraft, medical systems and financial systems. AI systems should equal or surpass human performance, but given the consequences of failure or erroneous or unfair decisions in these systems, how do we assure the public that these systems work as intended and will not cause harm? For example, that an autonomous vehicle does not crash or that intelligent credit scoring system is not biased, even after passing substantial acceptance testing prior to release. In this paper we discuss AI trust and assurance and related concepts, that is, assured autonomy, particularly for critical systems. Then we discuss how to establish trust through AI assurance activities throughout the system development lifecycle. Finally, we introduce a 'trust but verify continuously' approach to AI assurance, which describes assured autonomy activities in a model based systems development context and includes postdelivery activities for continuous assurance.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 29th Annual Software Technology Conference, STC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-180
Number of pages7
ISBN (Electronic)9781665488648
DOIs
StatePublished - 2022
Event29th IEEE Annual Software Technology Conference, STC 2022 - Virtual, Online, United States
Duration: Oct 3 2022Oct 6 2022

Publication series

NameProceedings - 2022 IEEE 29th Annual Software Technology Conference, STC 2022

Conference

Conference29th IEEE Annual Software Technology Conference, STC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/3/2210/6/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Safety, Risk, Reliability and Quality

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