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Recent studies on disparate impact in machine learning applications havesparked a debate around the concept of fairness along with attempts toformalize its different criteria. Many of these approaches focus on reducingprediction errors while maximizing sole utility of the institution. This workseeks to reconceptualize and critically frame the existing discourse onfairness by underlining the implicit biases embedded in common understandingsof fairness in the literature and how they contrast with its correspondingeconomic and legal definitions. This paper expands the concept of utility andfairness by bringing in concepts from established literature in welfareeconomics and game theory. We then translate these concepts for the algorithmicprediction domain by defining a formalization of Nash Welfare Product thatseeks to expand utility by collapsing that of the institution using theprediction tool and the individual subject to the prediction into one function.We then apply a modulating function that makes the fairness and welfaretrade-offs explicit based on designated policy goals and then apply it to atemporal model to take into account the effects of decisions beyond the scopeof one-shot predictions. We apply this on a binary classification problem andpresent results of a multi-epoch simulation based on the UCI Adult Incomedataset and a test case analysis of the ProPublica recidivism dataset that showthat expanding the concept of utility results in a fairer distributioncorrecting for the embedded biases in the dataset without sacrificing theclassifier accuracy.

Fairness for Whom? Critically reframing fairness with Nash Welfare Product