Measuring AI Systems Beyond Accuracy

TL;DR

This paper advocates for a comprehensive, integrated approach to testing AI systems, emphasizing the need for cross-domain evaluation methods to improve reliability beyond traditional accuracy metrics.

Contribution

It introduces six key questions to guide a holistic testing and evaluation strategy for AI systems, promoting a more complete assessment framework.

Findings

Highlights limitations of current T&E methods

Proposes a set of guiding questions for holistic evaluation

Encourages cross-domain and lifecycle-aware testing approaches

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.