Intrinsic Meets Extrinsic Fairness: Assessing the Downstream Impact of Bias Mitigation in Large Language Models
This work addresses fairness issues in LLMs for real-world financial applications, such as salary prediction and creditworthiness assessment, offering practical guidance for mitigation, but it is incremental as it builds on prior studies of bias in LLMs.
This paper tackles the problem of socio-economic biases in Large Language Models (LLMs) propagating into downstream tasks by empirically investigating the connection between intrinsic and extrinsic bias mitigation. The results show that intrinsic bias mitigation via unlearning reduces gender bias by up to 94.9% and improves downstream fairness metrics like demographic parity by up to 82% without compromising accuracy.
Large Language Models (LLMs) exhibit socio-economic biases that can propagate into downstream tasks. While prior studies have questioned whether intrinsic bias in LLMs affects fairness at the downstream task level, this work empirically investigates the connection. We present a unified evaluation framework to compare intrinsic bias mitigation via concept unlearning with extrinsic bias mitigation via counterfactual data augmentation (CDA). We examine this relationship through real-world financial classification tasks, including salary prediction, employment status, and creditworthiness assessment. Using three open-source LLMs, we evaluate models both as frozen embedding extractors and as fine-tuned classifiers. Our results show that intrinsic bias mitigation through unlearning reduces intrinsic gender bias by up to 94.9%, while also improving downstream task fairness metrics, such as demographic parity by up to 82%, without compromising accuracy. Our framework offers practical guidance on where mitigation efforts can be most effective and highlights the importance of applying early-stage mitigation before downstream deployment.