FARF: A Fair and Adaptive Random Forests Classifier

TL;DR

FARF introduces an adaptive online ensemble classifier that balances fairness and accuracy in real-time data streams, addressing biases in AI models with minimal hyperparameter tuning.

Contribution

The paper presents FARF, a novel online ensemble algorithm that dynamically adjusts for fairness and accuracy with a single hyperparameter, suitable for evolving data streams.

Findings

FARF effectively balances fairness and accuracy in real-world data streams.

Experiments show FARF outperforms existing methods in fairness metrics.

FARF requires only one hyperparameter for fairness-accuracy trade-off.

Abstract

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.