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.
@article{arxiv.2204.04211,
title = {Measuring AI Systems Beyond Accuracy},
author = {Violet Turri and Rachel Dzombak and Eric Heim and Nathan VanHoudnos and Jay Palat and Anusha Sinha},
journal= {arXiv preprint arXiv:2204.04211},
year = {2022}
}
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8 pages, Presented at 2022 AAAI Spring Symposium Series Workshop on AI Engineering: Creating Scalable, Human-Centered and Robust AI Systems