End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates

Recommender Systems (RS) have proven successful in a wide variety of domains, and the human resources (HR) domain is no exception. RS proved valuable for recommending candidates for a position, although the ethical implications have recently been identified as high-risk by the European Commission. In this study, we apply RS to match candidates with job requests. The RS pipeline includes two fairness gates at two different steps: pre-processing (using GAN-based synthetic candidate generation) and post-processing (with greedily searched candidate re-ranking). While prior research studied fairness at pre- and post-processing steps separately, our approach combines them both in the same pipeline applicable to the HR domain. We show that the combination of gender-balanced synthetic training data with pair re-ranking increased fairness with satisfactory levels of ranking utility. Our findings show that using only the gender-balanced synthetic data for bias mitigation is fairer by a negligible margin when compared to using real data. However, when implemented together with the pair re-ranker, candidate recommendation fairness improved considerably, while maintaining a satisfactory utility score. In contrast, using only the pair re-ranker achieved a similar fairness level, but had a consistently lower utility