Self-assessment for real-time performance estimation in AI/ML (Conference Presentation)

Peter A. Torrione

doi:10.1117/12.2666001

12 June 2023 Self-assessment for real-time performance estimation in AI/ML (Conference Presentation)

Peter A. Torrione

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 1253808 (2023) https://doi.org/10.1117/12.2666001
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

In this work we present a new concept we call self-assessment which leverages techniques from the self-supervision literature to estimate algorithm performance and certainty in real-time. Our self-assessment approach enables AI/ML systems to determine what they know, how well they know it, identify what is fundamentally knowable about a scene, and highlight confusing or un-reliable data for further investigation. This approach has applications to identifying out-of-domain data or flagging unexpected changes in operational contexts that can otherwise reduce AI/ML trustworthiness.

Conference Presentation

Citation Download Citation

Peter A. Torrione "Self-assessment for real-time performance estimation in AI/ML (Conference Presentation)", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 1253808 (12 June 2023); https://doi.org/10.1117/12.2666001

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