The article presents an analysis of the inductive biases inherent in fair supervised learning algorithms that pursue demographic parity (DP). It introduces a distributionally robust optimization approach to mitigate biases against majority sensitive attributes. The authors suggest further research into biases in pre-processing and post-processing methods in fair learning. The work also aims to theoretically compare different dependence measures and understand the trade-off between accuracy and fairness violations in DP-based fair learning scenarios, indicating its significance in future explorations.
In this work, we attempted to demonstrate the inductive biases of in-processing fair learning algorithms aiming to achieve demographic parity (DP).
#fairness-in-machine-learning #demographic-parity #fair-supervised-learning #optimization-techniques #inductive-biases
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