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Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey

Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Yichen Wang, Kuofeng Gao, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Shenghao Wu, Zongxing Xie, Weimin Lyu, Sihong He, Lu Cheng, Haohan Wang, Jun Zhuang

TL;DR

The paper addresses ethical challenges in large language models by separating persistent issues (privacy, copyright, fairness) from new-emerging concerns (truthfulness, social norms, regulation). It synthesizes existing research on risk understanding, mitigation strategies (DP methods, watermarking, debiasing, alignment), and governance frameworks. The authors propose a taxonomy and highlight gaps in robustness and regulation, advocating for embedding societal values into LLM development. This work informs researchers and policymakers on practical paths toward responsible and ethically aligned LLM deployment.

Abstract

Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.

Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey

TL;DR

The paper addresses ethical challenges in large language models by separating persistent issues (privacy, copyright, fairness) from new-emerging concerns (truthfulness, social norms, regulation). It synthesizes existing research on risk understanding, mitigation strategies (DP methods, watermarking, debiasing, alignment), and governance frameworks. The authors propose a taxonomy and highlight gaps in robustness and regulation, advocating for embedding societal values into LLM development. This work informs researchers and policymakers on practical paths toward responsible and ethically aligned LLM deployment.

Abstract

Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.
Paper Structure (19 sections, 7 figures)

This paper contains 19 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Persistent Ethical Issues
  3. Data Privacy
  4. Privacy: Issues and Challenges
  5. Differentially Private LLMs
  6. Other emerging methods
  7. Copyright
  8. Backdoor
  9. Watermark
  10. Fairness
  11. Mitigation Strategy
  12. Measurements on Fairness
  13. New-emerging Ethical Issues
  14. Truthfulness
  15. Hallucination
  16. ...and 4 more sections

Figures (7)

  • Figure 1: Main category in this survey paper.
  • Figure 2: The hierarchy of longstanding ethical problems in Section \ref{['sec:lei']}. We list corresponding mitigation strategies for each sub-category.
  • Figure 3: Data privacy issues & challenges detailed categories and mitigation methods.
  • Figure 4: Copyright methods.
  • Figure 5: The hierarchy of new-emerging ethical problems in Section \ref{['sec:nei']}. We list the ethical issues and corresponding mitigation strategies for each sub-category.
  • ...and 2 more figures

Theorems & Definitions (1)

  • Definition 1