Measuring Competency of Machine Learning Systems and Enforcing Reliability
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Abstract (arXiv)
We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 2 Dec 2022.
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References · 3
- 2013Intriguing properties of neural networkscited by 5,155
- 2020How Neural Networks Extrapolate: From Feedforward to Graph Neural Networkscited by 222
- 2020Principles and Practice of Explainable Machine Learningcited by 99
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