FARF: A Fair and Adaptive Random Forests Classifier
This work addresses the need for fair decision-making in evolving online settings, which is an incremental improvement over existing offline fairness methods.
The authors tackled the problem of achieving fairness in online machine learning by proposing FARF, a fair and adaptive random forests classifier that balances accuracy and fairness with a single hyperparameter, demonstrating its utility on real-world discriminated data streams.
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyperparameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.