Emergent Mind
Measuring AI Systems Beyond Accuracy
(2204.04211)
Published Apr 7, 2022
in
cs.SE
,
cs.AI
,
and
cs.LG
Abstract
Current test and evaluation (T&E) methods for assessing 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 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.
We're not able to analyze this paper right now due to high demand.
Please check back later (sorry!).
Generate a summary of this paper on our Pro plan:
We ran into a problem analyzing this paper.