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The increasing application of Artificial Intelligence and Machine Learningmodels poses potential risks of unfair behavior and, in light of recentregulations, has attracted the attention of the research community. Severalresearchers focused on seeking new fairness definitions or developingapproaches to identify biased predictions. However, none try to exploit thecounterfactual space to this aim. In that direction, the methodology proposedin this work aims to unveil unfair model behaviors using counterfactualreasoning in the case of fairness under unawareness setting. A counterfactualversion of equal opportunity named counterfactual fair opportunity is definedand two novel metrics that analyze the sensitive information of counterfactualsamples are introduced. Experimental results on three different datasets showthe efficacy of our methodologies and our metrics, disclosing the unfairbehavior of classic machine learning and debiasing models.

Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning