Measuring AI Systems Beyond Accuracy
| Autor: | Turri, Violet, Dzombak, Rachel, Heim, Eric, VanHoudnos, Nathan, Palat, Jay, Sinha, Anusha |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, Presented at 2022 AAAI Spring Symposium Series Workshop on AI Engineering: Creating Scalable, Human-Centered and Robust AI Systems |
| Databáze: | arXiv |
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