  We propose the use of Agent Based Models (ABMs) inside a reinforcement
learning framework in order to better understand the relationship between
automated decision making tools, fairness-inspired statistical constraints, and
the social phenomena giving rise to discrimination towards sensitive groups.
There have been many instances of discrimination occurring due to the
applications of algorithmic tools by public and private institutions. Until
recently, these practices have mostly gone unchecked. Given the large-scale
transformation these new technologies elicit, a joint effort of social sciences
and machine learning researchers is necessary. Much of the research has been
done on determining statistical properties of such algorithms and the data they
are trained on. We aim to complement that approach by studying the social
dynamics in which these algorithms are implemented. We show how bias can be
accumulated and reinforced through automated decision making, and the
possibility of finding a fairness inducing policy. We focus on the case of
recidivism risk assessment by considering simplified models of arrest. We find
that if we limit our attention to what is observed and manipulated by these
algorithmic tools, we may determine some blatantly unfair practices as fair,
illustrating the advantage of analyzing the otherwise elusive property with a
system-wide model. We expect the introduction of agent based simulation
techniques will strengthen collaboration with social scientists, arriving at a
better understanding of the social systems affected by technology and to
hopefully lead to concrete policy proposals that can be presented to
policymakers for a true systemic transformation.
