  Recent studies on disparate impact in machine learning applications have
sparked a debate around the concept of fairness along with attempts to
formalize its different criteria. Many of these approaches focus on reducing
prediction errors while maximizing sole utility of the institution. This work
seeks to reconceptualize and critically frame the existing discourse on
fairness by underlining the implicit biases embedded in common understandings
of fairness in the literature and how they contrast with its corresponding
economic and legal definitions. This paper expands the concept of utility and
fairness by bringing in concepts from established literature in welfare
economics and game theory. We then translate these concepts for the algorithmic
prediction domain by defining a formalization of Nash Welfare Product that
seeks to expand utility by collapsing that of the institution using the
prediction tool and the individual subject to the prediction into one function.
We then apply a modulating function that makes the fairness and welfare
trade-offs explicit based on designated policy goals and then apply it to a
temporal model to take into account the effects of decisions beyond the scope
of one-shot predictions. We apply this on a binary classification problem and
present results of a multi-epoch simulation based on the UCI Adult Income
dataset and a test case analysis of the ProPublica recidivism dataset that show
that expanding the concept of utility results in a fairer distribution
correcting for the embedded biases in the dataset without sacrificing the
classifier accuracy.
