Fairness Perceptions in Regression-based Predictive Models

TL;DR

This paper introduces new divergence-based fairness notions for regression models in kidney transplantation, evaluates social fairness preferences, and finds a preference for separation and sufficiency fairness criteria, highlighting disparities across age groups.

Contribution

It proposes three novel fairness notions for regression models and assesses social fairness preferences through crowd feedback in the context of organ transplantation.

Findings

Strong preference for separation and sufficiency fairness notions

Predictive models are fair for gender and race but not for age groups

Crowd feedback indicates social acceptance of certain fairness criteria

Abstract

Regression-based predictive analytics used in modern kidney transplantation is known to inherit biases from training data. This leads to social discrimination and inefficient organ utilization, particularly in the context of a few social groups. Despite this concern, there is limited research on fairness in regression and its impact on organ utilization and placement. This paper introduces three novel divergence-based group fairness notions: (i) independence, (ii) separation, and (iii) sufficiency to assess the fairness of regression-based analytics tools. In addition, fairness preferences are investigated from crowd feedback, in order to identify a socially accepted group fairness criterion for evaluating these tools. A total of 85 participants were recruited from the Prolific crowdsourcing platform, and a Mixed-Logit discrete choice model was used to model fairness feedback and estimate social fairness preferences. The findings clearly depict a strong preference towards the separation and sufficiency fairness notions, and that the predictive analytics is deemed fair with respect to gender and race groups, but unfair in terms of age groups.