Preprint
DEAN: DEACTIVATING THE COUPLED NEURONS TO
MITIGATE FAIRNESS-PRIVACY CONFLICTS IN LARGE
LANGUAGE MODELS
Chen Qian
1,2⋆
, Dongrui Liu
2⋆
, Jie Zhang
3,2
, Yong Liu
1†
, Jing Shao
2†
1
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
2
Shanghai Artificial Intelligence Laboratory, Shanghai, China
3
University of Chinese Academy of Sciences, Beijing, China
qianchen2022@ruc.edu.cn, liudongrui@pjlab.org.cn, zhangjie@iie.ac.cn
liuyonggsai@ruc.edu.cn, shaojing@pjlab.org.cn
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 DEActivate the fairness and privacy coupled Neurons (DEAN),
which theoretically and empirically decrease the mutual information between
fairness and privacy awareness. Extensive experimental results demonstrate that
DEAN eliminates the trade-off phenomenon and significantly improves LLMs’ fair-
ness and privacy awareness simultaneously, e.g., improving Qwen-2-7B-Instruct’s
fairness awareness by 12.2% and privacy awareness by 14.0%. More crucially,
DEAN 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. 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/DEAN.
1 INTRODUCTION
In recent years, as LLMs increasingly permeate sensitive areas such as healthcare, finance, and
education (Li et al., 2023b; Yuan et al., 2023; Al-Smadi, 2023), concerns regarding their fairness
and privacy implications have become critically important (Liu et al., 2023; Sun et al., 2024a). For
instance, when queried for sensitive information such as a social security number, we would expect
the LLM to refuse to provide such information. Similarly, a desirable LLM should avoid producing
unfair or discriminatory content, as shown in Figure 1(a).
In this paper, we focus on LLMs’ awareness of fairness and privacy concerns, i.e., their ability to
recognize and appropriately respond to requests involving sensitive information (Li et al., 2024; Sun
et al., 2024a). A well-recognized challenge is the trade-off between addressing fairness and privacy-
related concerns (Bagdasaryan et al., 2019; Mangold et al., 2023; Agarwal, 2021) in traditional Deep
Neural Networks (DNNs). As a result, many studies have emerged attempting to reconcile this
trade-off, proposing techniques to balance these conflicting objectives (Lyu et al., 2020; Cummings
et al., 2019). This prompts us to explore an intriguing question: Does trade-off also exist between the
awareness of fairness and privacy in the era of LLMs?
Interestingly, our preliminary experimental results indicate that enhancing privacy awareness through
SFT methods decreases the fairness awareness of LLMs, as shown in Figure 1(b). Specifically, we
fine-tune LLMs on limited-data conditions (thousands of samples) with Full-parameter Fine-Tuning
⋆
Equal contribution
†
Corresponding author
1
arXiv:2410.16672v1 [cs.AI] 22 Oct 2024
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