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Algorithmic fairness has emphasized the role of biased data in automateddecision outcomes. Recently, there has been a shift in attention to sources ofbias that implicate fairness in other stages in the ML pipeline. We contendthat one source of such bias, human preferences in model selection, remainsunder-explored in terms of its role in disparate impact across demographicgroups. Using a deep learning model trained on real-world medical imaging data,we verify our claim empirically and argue that choice of metric for modelcomparison, especially those that do not take variability into account, cansignificantly bias model selection outcomes.

Model Selection's Disparate Impact in Real-World Deep Learning Applications