Counterfactual fairness is a definition of fairness that states that an algorithm is counterfactual fair if its results for an individual would be the same in a world where she belongs to another (counterfactual) sociodemographic group. An algorithm that is used for recruitment is counterfactual fair if selecting a person for a job interview is equally probable if (s)he were a man, woman, ethnic minority or ethnic majority. The concept of counterfactual fairness is mostly studied using causal modeling. Importantly, confounding or proxy variables that are related to the sociodemographic sensitive attributes need to be taken into account.


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