Abstract
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