  The influence of human judgement is ubiquitous in datasets used across the
analytics industry, yet humans are known to be sub-optimal decision makers
prone to various biases. Analysing biased datasets then leads to biased
outcomes of the analysis. Bias by protected characteristics (e.g. race) is of
particular interest as it may not only make the output of analytical process
sub-optimal, but also illegal. Countering the bias by constraining the
analytical outcomes to be fair is problematic because A) fairness lacks a
universally accepted definition, while at the same time some definitions are
mutually exclusive, and B) the use of optimisation constraints ensuring
fairness is incompatible with most analytical pipelines. Both problems are
solved by methods which remove bias from the data and returning an altered
dataset. This approach aims to not only remove the actual bias variable (e.g.
race), but also alter all proxy variables (e.g. postcode) so the bias variable
is not detectable from the rest of the data. The advantage of using this
approach is that the definition of fairness as a lack of detectable bias in the
data (as opposed to the output of analysis) is universal and therefore solves
problem (A). Furthermore, as the data is altered to remove bias the problem (B)
disappears because the analytical pipelines can remain unchanged. This approach
has been adopted by several technical solutions. None of them, however, seems
to be satisfactory in terms of ability to remove multivariate, non-linear and
non-binary biases. Therefore, in this paper I propose the concept of Fair
Adversarial Networks as an easy-to-implement general method for removing bias
from data. This paper demonstrates that Fair Adversarial Networks achieve this
aim.
