The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models

TL;DR

This paper introduces SPIN, a training-free method inspired by information theory, to simultaneously improve fairness and privacy awareness in large language models, overcoming a trade-off observed with traditional fine-tuning methods.

Contribution

The paper proposes SPIN, a novel training-free approach that reduces the mutual information between fairness and privacy neurons, effectively mitigating their trade-off in LLMs.

Findings

SPIN improves fairness awareness by 12.2%.

SPIN enhances privacy awareness by 14.0%.

SPIN remains effective with limited or malicious data.

Abstract

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's privacy awareness through Supervised Fine-Tuning (SFT) methods significantly decreases its fairness awareness with thousands of samples. To address this issue, inspired by the information theory, we introduce a training-free method to \textbf{S}uppress the \textbf{P}rivacy and fa\textbf{I}rness coupled \textbf{N}eurons (\textbf{SPIN}), which theoretically and empirically decrease the mutual information between fairness and privacy awareness. Extensive experimental results demonstrate that SPIN eliminates the trade-off phenomenon and significantly improves LLMs' fairness and privacy awareness simultaneously without compromising general capabilities, \eg improving Qwen-2-7B-Instruct's fairness awareness by 12.2\% and privacy awareness by 14.0\%. More crucially, SPIN remains robust and effective with limited annotated data or even when only malicious fine-tuning data is available, whereas SFT methods may fail to perform properly in such scenarios. Furthermore, we show that SPIN could generalize to other potential trade-off dimensions. We hope this study provides valuable insights into concurrently addressing fairness and privacy concerns in LLMs and can be integrated into comprehensive frameworks to develop more ethical and responsible AI systems. Our code is available at https://github.com/ChnQ/SPIN.