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arXiv · 2204.04211

Measuring AI Systems Beyond Accuracy

Violet Turri·Rachel Dzombak·Eric Heim·Nathan VanHoudnos·Jay Palat·Anusha Sinha
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Abstract

Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the other phases of the ML system lifecycle. Research investigating cross-domain approaches to ML T&E is needed to drive the state of the art forward and to build an Artificial Intelligence (AI) engineering discipline. This paper advocates for a robust, integrated approach to testing by outlining six key questions for guiding a holistic T&E strategy.

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Violet Turri, Rachel Dzombak, Eric Heim, Nathan VanHoudnos, Jay Palat, Anusha Sinha. 2022-04-07. Measuring AI Systems Beyond Accuracy. https://arxiv.org/abs/2204.04211

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