The Equity Framework: Fairness Beyond Equalized Predictive Outcomes
Keziah Naggita, J. Ceasar Aguma

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…
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Taxonomy
TopicsEthics and Social Impacts of AI
