Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper analyzes predictive multiplicity, the phenomenon of multiple AI models with similar overall accuracy disagreeing on individual predictions, in the context of the EU AI Act. It argues that high predictive multiplicity violates the Act's requirements for individual-level performance reporting, as it introduces arbitrariness in decisions impacting humans. The paper proposes individual conflict ratios and $\delta$-ambiguity as metrics to quantify disagreement between models on individual cases and offers practical guidelines for model providers to evaluate and report predictive multiplicity.
The EU AI Act's focus on individual accuracy clashes with the reality that many AI systems exhibit "predictive multiplicity," where equally accurate models disagree wildly on specific cases, potentially undermining fairness and accountability.
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.