Using predictive multiplicity to measure individual performance within the AI Act

TL;DR

This paper examines predictive multiplicity in AI decision systems, highlighting its implications for individual fairness and compliance with the EU AI Act, and proposes practical tools for evaluating and reporting model disagreement on individual cases.

Contribution

It introduces the concept of predictive multiplicity in the context of the EU AI Act and proposes new metrics and guidelines for assessing individual-level model disagreement.

Findings

Predictive multiplicity can be quantified using individual conflict ratios and δ-ambiguity.

Incorporating predictive multiplicity metrics can improve compliance with the EU AI Act.

Practical rules for evaluating model disagreement on individual cases are proposed.

Abstract

When building AI systems for decision support, one often encounters the phenomenon of predictive multiplicity: a single best model does not exist; instead, one can construct many models with similar overall accuracy that differ in their predictions for individual cases. Especially when decisions have a direct impact on humans, this can be highly unsatisfactory. For a person subject to high disagreement between models, one could as well have chosen a different model of similar overall accuracy that would have decided the person's case differently. We argue that this arbitrariness conflicts with the EU AI Act, which requires providers of high-risk AI systems to report performance not only at the dataset level but also for specific persons. The goal of this paper is to put predictive multiplicity in context with the EU AI Act's provisions on accuracy and to subsequently derive concrete suggestions on how to evaluate and report predictive multiplicity in practice. Specifically: (1) We argue that incorporating information about predictive multiplicity can serve compliance with the EU AI Act's accuracy provisions for providers. (2) Based on this legal analysis, we suggest individual conflict ratios and $\delta$-ambiguity as tools to quantify the disagreement between models on individual cases and to help detect individuals subject to conflicting predictions. (3) Based on computational insights, we derive easy-to-implement rules on how model providers could evaluate predictive multiplicity in practice. (4) Ultimately, we suggest that information about predictive multiplicity should be made available to deployers under the AI Act, enabling them to judge whether system outputs for specific individuals are reliable enough for their use case.