2026-09-20T15:09:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1584742025-04-07T09:05:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Barzilay, Regina
Alexiev, Christopher
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-03-12T16:54:28Z
2025-03-12T16:54:28Z
2024-09
2025-03-04T18:44:18.166Z
https://hdl.handle.net/1721.1/158474
https://orcid.org/0000-0002-6827-4994
Biases in artificial intelligence systems and the data they operate over are a major hurdle to their application in clinical and biomedical settings. Such systems have frequently been shown to fail to generalize from their training data to the real world environment and often display differing levels of accuracy over different population subgroups, which has detrimental effects on patients' quality of care and on healthcare equality. Here, we introduce an automated framework for identifying and understanding nontrivial sources of bias in healthcare datasets and AI models. Our framework is data and model agnostic and does not rely on human-developed heuristics or assumptions to uncover bias. We demonstrate its effectiveness by uncovering serious and nontrivial sources of bias in three widely used clinical datasets and one biomedical dataset, over the diverse tasks of diabetes risk prediction, lung cancer risk prediction, and biomolecular toxicity prediction. Our framework is used to uncover biases caused by patient BMI and computed tomography (CT) scanner type in the data used by a cutting-edge lung cancer risk prediction AI model, causing AUC drops on the order of ten percent.
S.M.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Interpretable and Automated Bias Detection for AI in Healthcare
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Biases in artificial intelligence systems and the data they operate over are a major hurdle to their application in clinical and biomedical settings. Such systems have frequently been shown to fail to generalize from their training data to the real world environment and often display differing levels of accuracy over different population subgroups, which has detrimental effects on patients' quality of care and on healthcare equality. Here, we introduce an automated framework for identifying and understanding nontrivial sources of bias in healthcare datasets and AI models. Our framework is data and model agnostic and does not rely on human-developed heuristics or assumptions to uncover bias. We demonstrate its effectiveness by uncovering serious and nontrivial sources of bias in three widely used clinical datasets and one biomedical dataset, over the diverse tasks of diabetes risk prediction, lung cancer risk prediction, and biomolecular toxicity prediction. Our framework is used to uncover biases caused by patient BMI and computed tomography (CT) scanner type in the data used by a cutting-edge lung cancer risk prediction AI model, causing AUC drops on the order of ten percent.</Abstract>
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