  The wide spread usage of automated data-driven decision support systems has
raised a lot of concerns regarding accountability and fairness of the employed
models in the absence of human supervision. Existing fairness-aware approaches
tackle fairness as a batch learning problem and aim at learning a fair model
which can then be applied to future instances of the problem. In many
applications, however, the data comes sequentially and its characteristics
might evolve with time. In such a setting, it is counter-intuitive to "fix" a
(fair) model over the data stream as changes in the data might incur changes in
the underlying model therefore, affecting its fairness. In this work, we
propose fairness-enhancing interventions that modify the input data so that the
outcome of any stream classifier applied to that data will be fair. Experiments
on real and synthetic data show that our approach achieves good predictive
performance and low discrimination scores over the course of the stream.
