Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework

• URL: http://arxiv.org/abs/2503.09969v2
• Date: Tue, 03 Jun 2025 20:18:36 GMT
• Title: Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
• Authors: Nathan Drenkow, Mitchell Pavlak, Keith Harrigian, Ayah Zirikly, Adarsh Subbaswamy, Mohammad Mehdi Farhangi, Nicholas Petrick, Mathias Unberath,
• Abstract summary: Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT) for datasets is a modality-agnostic dataset auditing framework.<n>Our method examines the relationship between task-level annotations and data properties including patient attributes.<n>G-AUDIT successfully identifies subtle biases commonly overlooked by traditional qualitative methods.
• Score: 8.017827642932746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major reason for these limitations is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and/or hide it during testing. To unlock new tools capable of foreseeing and preventing such AI bias issues, we present G-AUDIT. Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT) for datasets is a modality-agnostic dataset auditing framework that allows for generating targeted hypotheses about sources of bias in training or testing data. Our method examines the relationship between task-level annotations (commonly referred to as ``labels'') and data properties including patient attributes (e.g., age, sex) and environment/acquisition characteristics (e.g., clinical site, imaging protocols). G-AUDIT quantifies the extent to which the observed data attributes pose a risk for shortcut learning, or in the case of testing data, might hide predictions made based on spurious associations. We demonstrate the broad applicability of our method by analyzing large-scale medical datasets for three distinct modalities and machine learning tasks: skin lesion classification in images, stigmatizing language classification in Electronic Health Records (EHR), and mortality prediction for ICU tabular data. In each setting, G-AUDIT successfully identifies subtle biases commonly overlooked by traditional qualitative methods, underscoring its practical value in exposing dataset-level risks and supporting the downstream development of reliable AI systems.

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