Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring

Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly, Evangelos Anagnostopoulos, Christos Varytimidis, Antonio Del rio chanona

Keywords: Fairness in Downstream Tasks, Algorithmic Hiring, Addressing Bias and Fairness

Abstract:

Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human feedback that can be expensive to acquire.
We present AutoRefine, a method that leverages reinforcement learning for targeted fine-tuning, utilizing direct feedback from measurable performance improvements in specific downstream tasks. We demonstrate the method for a problem arising in algorithmic hiring platforms where linguistic biases influence a recommendation system. In this setting, a generative model seeks to rewrite given job specifications to receive more diverse candidate matches from a recommendation engine which matches jobs to candidates. Our model detects and regulates biases in job descriptions to meet diversity and fairness criteria. The experiments on a public hiring dataset and a real-world hiring platform showcase how large language models can assist in identifying and mitigation biases in the real world.

Submission Number: 21