On Arbitrary Predictions from Equally Valid Models

TL;DR

This paper investigates the phenomenon of predictive multiplicity in medical models, showing that multiple equally valid models can produce different predictions, and demonstrates that small ensembles can mitigate this issue to improve diagnostic reliability.

Contribution

It provides an empirical analysis of predictive multiplicity in medical models, highlighting its causes, consequences, and proposing ensemble strategies to address it.

Findings

Standard validation metrics do not identify a unique optimal model.

Predictive multiplicity can lead to arbitrary patient diagnoses.

Small ensembles with abstention strategies effectively reduce predictive multiplicity.

Abstract

Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In medicine, these models can admit conflicting predictions for the same patient -- a risk that is poorly understood and insufficiently addressed. In this study, we empirically analyze the extent, drivers, and ramifications of predictive multiplicity across diverse medical tasks and model architectures, and show that even small ensembles can mitigate/eliminate predictive multiplicity in practice. Our analysis reveals that (1) standard validation metrics fail to identify a uniquely optimal model and (2) a substantial amount of predictions hinges on arbitrary choices made during model development. Using multiple models instead of a single model reveals instances where predictions differ across equally plausible models -- highlighting patients that would receive arbitrary diagnoses if any single model were used. In contrast, (3) a small ensemble paired with an abstention strategy can effectively mitigate measurable predictive multiplicity in practice; predictions with high inter-model consensus may thus be amenable to automated classification. While accuracy is not a principled antidote to predictive multiplicity, we find that (4) higher accuracy achieved through increased model capacity reduces predictive multiplicity. Our findings underscore the clinical importance of accounting for model multiplicity and advocate for ensemble-based strategies to improve diagnostic reliability. In cases where models fail to reach sufficient consensus, we recommend deferring decisions to expert review.