Improving the Fairness of Chest X-ray Classifiers
- URL: http://arxiv.org/abs/2203.12609v1
- Date: Wed, 23 Mar 2022 17:56:58 GMT
- Title: Improving the Fairness of Chest X-ray Classifiers
- Authors: Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner,
Stephen Robert Pfohl, Marzyeh Ghassemi
- Abstract summary: We ask whether striving to achieve zero disparities in predictive performance (i.e. group fairness) is the appropriate fairness definition in the clinical setting.
We find, consistent with prior work on non-clinical data, that methods which strive to achieve better worst-group performance do not outperform simple data balancing.
- Score: 19.908277166053185
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning models have reached or surpassed human-level performance in the
field of medical imaging, especially in disease diagnosis using chest x-rays.
However, prior work has found that such classifiers can exhibit biases in the
form of gaps in predictive performance across protected groups. In this paper,
we question whether striving to achieve zero disparities in predictive
performance (i.e. group fairness) is the appropriate fairness definition in the
clinical setting, over minimax fairness, which focuses on maximizing the
performance of the worst-case group. We benchmark the performance of nine
methods in improving classifier fairness across these two definitions. We find,
consistent with prior work on non-clinical data, that methods which strive to
achieve better worst-group performance do not outperform simple data balancing.
We also find that methods which achieve group fairness do so by worsening
performance for all groups. In light of these results, we discuss the utility
of fairness definitions in the clinical setting, advocating for an
investigation of the bias-inducing mechanisms in the underlying data generating
process whenever possible.
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