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Measuring AI Systems Beyond Accuracy

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.

preprint2022arXivOpen access
Violet TurriRachel DzombakEric HeimNathan VanHoudnosJay PalatAnusha Sinha
Software EngineeringMachine LearningArtificial Intelligence
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Open access6 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Violet TurriRachel DzombakEric HeimNathan VanHoudnosJay PalatAnusha Sinha

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