  This paper considers the need for generalizable bias mitigation techniques in
machine learning due to the growing concerns of fairness and discrimination in
data-driven decision-making procedures across a range of industries. While many
existing methods for mitigating bias in machine learning have succeeded in
specific cases, they often lack generalizability and cannot be easily applied
to different data types or models. Additionally, the trade-off between accuracy
and fairness remains a fundamental tension in the field. To address these
issues, we propose a bias mitigation method based on multi-task learning,
utilizing the concept of Monte-Carlo dropout and Pareto optimality from
multi-objective optimization. This method optimizes accuracy and fairness while
improving the model's explainability without using sensitive information. We
test this method on three datasets from different domains and show how it can
deliver the most desired trade-off between model fairness and performance. This
allows for tuning in specific domains where one metric may be more important
than another. With the framework we introduce in this paper, we aim to enhance
the fairness-performance trade-off and offer a solution to bias mitigation
methods' generalizability issues in machine learning.
