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In the DARPA Competency Aware Machine Learning program, the MindfuL™ system derived machine level ‘conditions’ (topics in a multi-modal Hierarchical Dirichlet Process model) that are aligned with and predictive of the competency of a CNN obstacle detection component of a self-driving car. These competency controlling conditions don’t map directly into human level concepts, which limits the utility of this explainable AI approach. This paper discusses methods to increase understanding and trust of the competency assessment and the ML agent itself by automatically generating labels for these conditions that will be meaningful and useful to the human operator.
Olga Babko-Malaya,Michael Planer, andLetitia Li
"Assigning semantic meaning to machine derived competency controlling topics", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380L (12 June 2023); https://doi.org/10.1117/12.2663821
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Olga Babko-Malaya, Michael Planer, Letitia Li, "Assigning semantic meaning to machine derived competency controlling topics," Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380L (12 June 2023); https://doi.org/10.1117/12.2663821