As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …

To ensure equitable quality of care, differences in machine learning model performance
between patient groups must be addressed. Here, we argue that two separate mechanisms …

Since out-of-distribution generalization is a generally ill-posed problem, various proxy
targets (eg, calibration, adversarial robustness, algorithmic corruptions, invariance across …

A multitude of work has shown that machine learning-based medical diagnosis systems can
be biased against certain subgroups of people. This has motivated a growing number of …

Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …

Training models with robust group fairness properties is crucial in ethically sensitive
application areas such as medical diagnosis. Despite the growing body of work aiming to …

Machine learning is expected to fuel significant improvements in medical care. To ensure
that fundamental principles such as beneficence, respect for human autonomy, prevention of …

Published research has confirmed the bias of automated face-based gender classification
algorithms across gender-racial groups. Specifically, unequal accuracy rates were obtained …

As artificial intelligence (AI) rapidly approaches human-level performance in medical
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous …

We tackle the problem of group fairness in classification, where the objective is to learn
models that do not unjustly discriminate against subgroups of the population. Most existing …