FairNN- Conjoint Learning of Fair Representations for Fair Decisions
This addresses fairness in machine learning decisions for domains like hiring or lending, but it is incremental as it builds on existing fairness methods.
The paper tackles the problem of fairness-aware learning by proposing FairNN, a neural network that jointly learns fair representations and performs classification, achieving superior performance compared to separate treatments of unfairness in representation or supervised learning.
In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularized. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.