Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
This addresses fairness issues in algorithmic hiring platforms, though it is an incremental improvement using existing methods on a specific domain problem.
The paper tackles the challenge of fine-tuning foundation models for fairness in algorithmic hiring by introducing AutoRefine, a reinforcement learning method that uses measurable performance improvements for targeted fine-tuning. The method successfully reduces linguistic biases in job descriptions, increasing diverse candidate matches by 15-20% on a public dataset and a real-world platform.
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.