Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning

TL;DR

This paper introduces a novel counterfactual fairness metric called counterfactual fair opportunity, which detects unfair behaviors in decision models by analyzing counterfactual samples, revealing biases in existing models.

Contribution

It proposes a new counterfactual fairness measure and metrics to identify unfairness in models, addressing a gap in current fairness evaluation methods.

Findings

Counterfactual fair opportunity effectively uncovers unfair behaviors.

Metrics reveal biases in classic and debiased models.

Experimental results validate the approach across datasets.

Abstract

The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. However, none try to exploit the counterfactual space to this aim. In that direction, the methodology proposed in this work aims to unveil unfair model behaviors using counterfactual reasoning in the case of fairness under unawareness setting. A counterfactual version of equal opportunity named counterfactual fair opportunity is defined and two novel metrics that analyze the sensitive information of counterfactual samples are introduced. Experimental results on three different datasets show the efficacy of our methodologies and our metrics, disclosing the unfair behavior of classic machine learning and debiasing models.