  Fairness and accountability are two essential pillars for trustworthy
Artificial Intelligence (AI) in healthcare. However, the existing AI model may
be biased in its decision marking. To tackle this issue, we propose an
adversarial multi-task training strategy to simultaneously mitigate and detect
bias in the deep learning-based medical image analysis system. Specifically, we
propose to add a discrimination module against bias and a critical module that
predicts unfairness within the base classification model. We further impose an
orthogonality regularization to force the two modules to be independent during
training. Hence, we can keep these deep learning tasks distinct from one
another, and avoid collapsing them into a singular point on the manifold.
Through this adversarial training method, the data from the underprivileged
group, which is vulnerable to bias because of attributes such as sex and skin
tone, are transferred into a domain that is neutral relative to these
attributes. Furthermore, the critical module can predict fairness scores for
the data with unknown sensitive attributes. We evaluate our framework on a
large-scale public-available skin lesion dataset under various fairness
evaluation metrics. The experiments demonstrate the effectiveness of our
proposed method for estimating and improving fairness in the deep
learning-based medical image analysis system.
