Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation

Autor: Rus, Clara, Luppes, Jeffrey, Oosterhuis, Harrie, Schoenmacker, Gido H.

Publikováno v: RecSys in HR'22: The 2nd Workshop on Recommender Systems for Human Resources, in conjunction with the 16th ACM Conference on Recommender Systems, September 18-23, 2022, Seattle, USA

The goal of this work is to help mitigate the already existing gender wage gap by supplying unbiased job recommendations based on resumes from job seekers. We employ a generative adversarial network to remove gender bias from word2vec representations

Externí odkaz: http://arxiv.org/abs/2209.09592