[论文解读] Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models

Md Meftahul Ferdaus, Mahdi Abdelguerfi+4 more|arXiv (Cornell University)|Jun 1, 2024

Topic Modeling被引用 11

一句话总结

对LLMs信任的全面评估，考察伦理、技术和治理因素，并提出框架和指南以提高透明度、公平性和鲁棒性。

ABSTRACT

The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and building user trust. This comprehensive review looks at key trust issues in LLMs, such as unintended harms, lack of transparency, vulnerability to attacks, alignment with human values, and environmental impact. Many obstacles can undermine user trust, including societal biases, opaque decision-making, potential for misuse, and the challenges of rapidly evolving technology. Addressing these trust gaps is critical as LLMs become more common in sensitive areas like finance, healthcare, education, and policy. To tackle these issues, we suggest combining ethical oversight, industry accountability, regulation, and public involvement. AI development norms should be reshaped, incentives aligned, and ethics integrated throughout the machine learning process, which requires close collaboration across technology, ethics, law, policy, and other fields. Our review contributes a robust framework to assess trust in LLMs and analyzes the complex trust dynamics in depth. We provide contextualized guidelines and standards for responsibly developing and deploying these powerful AI systems. This review identifies key limitations and challenges in creating trustworthy AI. By addressing these issues, we aim to build a transparent, accountable AI ecosystem that benefits society while minimizing risks. Our findings provide valuable guidance for researchers, policymakers, and industry leaders striving to establish trust in LLMs and ensure they are used responsibly across various applications for the good of society.

研究动机与目标

• 评估LLMs中的信任挑战，包括危害、透明度、攻击、与人类价值观的一致性，以及环境影响。
• 提出一个跨多方观点的综合框架，用于评估LLMs的可信度。
• 提供有上下文的指南和标准，以引导LLMs的负责任开发和部署。

提出的方法

• 为LLMs的信任建立一个包含八个视角的鲁棒评估框架（透明度、鲁棒性、与人类价值的一致性、环境影响等）。
• 综合伦理、技术和政策考量，分析信任动态与治理需求。
• 结合可解释性（XAI）方法与日志记录以支持透明度和问责制。
• 使用案例研究来说明LLM信任度随时间的提升。

实验结果

研究问题

• RQ1 在高风险领域，LLMs面临的关键信任挑战是什么？
• RQ2 如何在伦理、技术和社会维度上全面评估LLMs的可信度？
• RQ3 哪些指南和标准可以将伦理原则落地到LLM开发和部署中？

主要发现

• LLMs在毒性、偏见、鲁棒性、隐私、伦理与公平方面仍存在持续关注。
• 最新更新在处理有害提示、刻板印象和对抗性输入方面对若干LLMs有所改进。
• 多维一致性框架评估可靠性、安全、公平、滥用防护、推理、社会规范和鲁棒性，揭示领先模型的信任对齐有所提升。
• 可解释性技术与日志记录对LLM系统的调试、审计和问责至关重要。
• 案例研究显示从早期的脆弱性到现代模型的更强鲁棒、可信赖行为的进步。