  Most datasets of interest to the analytics industry are impacted by various
forms of human bias. The outcomes of Data Analytics [DA] or Machine Learning
[ML] on such data are therefore prone to replicating the bias. As a result, a
large number of biased decision-making systems based on DA/ML have recently
attracted attention. In this paper we introduce Rosa, a free, web-based tool to
easily de-bias datasets with respect to a chosen characteristic. Rosa is based
on the principles of Fair Adversarial Networks, developed by illumr Ltd., and
can therefore remove interactive, non-linear, and non-binary bias. Rosa is
stand-alone pre-processing step / API, meaning it can be used easily with any
DA/ML pipeline. We test the efficacy of Rosa in removing bias from data-driven
decision making systems by performing standard DA tasks on five real-world
datasets, selected for their relevance to current DA problems, and also their
high potential for bias. We use simple ML models to model a characteristic of
analytical interest, and compare the level of bias in the model output both
with and without Rosa as a pre-processing step. We find that in all cases there
is a substantial decrease in bias of the data-driven decision making systems
when the data is pre-processed with Rosa.
