A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination
This addresses fairness gaps in algorithmic decision-making for stakeholders like loan applicants or defendants, though it builds incrementally on existing causal fairness methods.
The authors tackled the problem that fairness analyses in algorithmic decision-making often overlook non-binary treatment decisions (e.g., bail conditions or loan terms), proposing a causal framework to measure and mitigate their discriminatory impact. They empirically analyzed four loan approval datasets to reveal disparities and showed their framework effectively mitigates treatment discrimination to ensure fair risk score estimation.
Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.