Data preprocessing to mitigate bias: A maximum entropy based approach

Understand

Data containing human or social attributes may over- or under-represent groups with respect to salient social attributes such as gender or race, which can lead to biases in downstream applications.

• This paper presents an algorithmic framework that can be used as a data preprocessing method towards mitigating such bias.
• Unlike prior work, it can efficiently learn distributions over large domains, controllably adjust the representation rates of protected groups and achieve target fairness metrics such as statistical parity, yet remains close to the empirical distribution induced by the given dataset.
• Our approach leverages the principle of maximum entropy - amongst all distributions satisfying a given set of constraints, we should choose the one closest in KL-divergence to a given prior.