  Automated data-driven decision-making systems are ubiquitous across a wide
spread of online as well as offline services. These systems, depend on
sophisticated learning algorithms and available data, to optimize the service
function for decision support assistance. However, there is a growing concern
about the accountability and fairness of the employed models by the fact that
often the available historic data is intrinsically discriminatory, i.e., the
proportion of members sharing one or more sensitive attributes is higher than
the proportion in the population as a whole when receiving positive
classification, which leads to a lack of fairness in decision support system. A
number of fairness-aware learning methods have been proposed to handle this
concern. However, these methods tackle fairness as a static problem and do not
take the evolution of the underlying stream population into consideration. In
this paper, we introduce a learning mechanism to design a fair classifier for
online stream based decision-making. Our learning model, FAHT (Fairness-Aware
Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for
decision tree induction over streams, that also accounts for fairness. Our
experiments show that our algorithm is able to deal with discrimination in
streaming environments, while maintaining a moderate predictive performance
over the stream.
