Counterfactual Representations for Intersectional Fair Ranking in Recruitment

Fairness interventions require access to sensitive attributes of candidates applying for a job, which might not be available due to limitations imposed by data protection laws. In this work we propose using a pre-processing technique to create counterfactual representations of the candidates that lead to a more diverse ranking with respect to intersectional groups. To be compliant with data protection laws we propose to train a model on the fairer representations and apply the model at inference time without having access to the sensitive attributes of the candidates. In experiments on the BIOS dataset, we find this approach can improve the diversity of recommendations at top-ranked positions without harming performance.