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

• URL: http://arxiv.org/abs/2501.07324v1
• Date: Mon, 13 Jan 2025 13:36:17 GMT
• Title: Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
• Authors: Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly, Evangelos Anagnostopoulos, Christos Varytimidis, Antonio del Rio Chanona,
• Abstract summary: We present AutoRefine, a method that leverages reinforcement learning for targeted fine-tuning. We demonstrate the method for a problem arising in algorithmic hiring platforms where linguistic biases influence a recommendation system. Our model detects and regulates biases in job descriptions to meet diversity and fairness criteria.
• Score: 5.482898079941062
• License:
• 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.

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