The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

TL;DR

This paper introduces the Equity Framework, emphasizing access, outcomes, and utilization to promote fairness and social welfare in ML decision-making, moving beyond traditional equalized predictive outcomes.

Contribution

It proposes a novel Equity Framework that considers access, outcomes, and utilization, providing an algorithm and guidelines to achieve equitable ML decisions.

Findings

The framework improves social welfare over existing fairness notions.

Failure to consider access, outcome, and utilization worsens inequities.

An equity scoring algorithm guides fair decision-making.

Abstract

Machine Learning (ML) decision-making algorithms are now widely used in predictive decision-making, for example, to determine who to admit and give a loan. Their wide usage and consequential effects on individuals led the ML community to question and raise concerns on how the algorithms differently affect different people and communities. In this paper, we study fairness issues that arise when decision-makers use models (proxy models) that deviate from the models that depict the physical and social environment in which the decisions are situated (intended models). We also highlight the effect of obstacles on individual access and utilization of the models. To this end, we formulate an Equity Framework that considers equal access to the model, equal outcomes from the model, and equal utilization of the model, and consequentially achieves equity and higher social welfare than current fairness notions that aim for equality. We show how the three main aspects of the framework are connected and provide an equity scoring algorithm and questions to guide decision-makers towards equitable decision-making. We show how failure to consider access, outcome, and utilization would exacerbate proxy gaps leading to an infinite inequity loop that reinforces structural inequities through inaccurate and incomplete ground truth curation. We, therefore, recommend a more critical look at the model design and its effect on equity and a shift towards equity achieving predictive decision-making models.