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AI Assurance for the Public -- Trust but Verify, Continuously

Published

Author(s)

Phillip Laplante, D. Richard Kuhn

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? 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 post-delivery activities for continuous assurance.
Proceedings Title
2022 IEEE 29th Annual Software Technology Conference (STC)
Conference Dates
October 3-6, 2022
Conference Location
Gaithersburg, MD, US

Keywords

artificial intelligence, zero trust, explainable AI

Citation

Laplante, P. and Kuhn, D. (2022), AI Assurance for the Public -- Trust but Verify, Continuously, 2022 IEEE 29th Annual Software Technology Conference (STC), Gaithersburg, MD, US, [online], https://doi.org/10.1109/STC55697.2022.00032, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935075 (Accessed June 24, 2024)

Issues

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Created October 3, 2022, Updated September 28, 2023