  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.

Machine Learning;Artificial Intelligence