Auditor Models to Suppress Poor AI Predictions Can Improve Human-AI Collaborative Performance.

ABSTRACT:

Objective

Healthcare decisions are increasingly made with the assistance of machine learning (ML). ML has been known to have unfairness - inconsistent outcomes across subpopulations. Clinicians interacting with these systems can perpetuate such unfairness by overreliance. Recent work exploring ML suppression - silencing predictions based on auditing the ML - shows promise in mitigating performance issues originating from overreliance. This study aims to evaluate the impact of suppression on collaboration fairness and evaluate ML uncertainty as desiderata to audit the ML.

Materials and methods

We used data from the Vanderbilt University Medical Center electronic health record (n = 58,817) and the MIMIC-IV-ED dataset (n = 363,145) to predict likelihood of death or ICU transfer

SUBMITTER: Brown KE 

PROVIDER: S-EPMC12262782 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature