The rapid advancements in large language models (LLMs) have revolutionized natural language processing, unlocking unprecedented capabilities in communication, automation, and knowledge generation. However, the ethical implications of LLM development, particularly in data harnessing, remain a critical challenge. Despite widespread discussion about the ethical compliance of LLMs -- especially concerning their data harnessing processes, there remains a notable absence of concrete frameworks to systematically guide or measure the ethical risks involved. In this paper we discuss a potential pathway for building an Ethical Risk Scoring (ERS) system to quantitatively assess the ethical integrity of the data harnessing process for AI systems. This system is based on a set of assessment questions grounded in core ethical principles, which are, in turn, supported by commanding ethical theories. By integrating measurable scoring mechanisms, this approach aims to foster responsible LLM development, balancing technological innovation with ethical accountability.

翻译：大语言模型（LLMs）的快速发展已彻底变革自然语言处理领域，在沟通交流、自动化任务和知识生成等方面展现出前所未有的能力。然而，LLM发展过程中的伦理影响——特别是在数据利用环节——仍然是一个严峻挑战。尽管学界对LLM的伦理合规性（尤其是其数据利用过程）展开了广泛讨论，但目前仍缺乏能够系统指导或衡量相关伦理风险的具体框架。本文探讨了构建伦理风险评分（ERS）系统的可行路径，旨在对人工智能系统数据利用过程的伦理完整性进行定量评估。该系统基于一组根植于核心伦理原则的评估问题，而这些原则又由权威伦理理论提供支撑。通过整合可量化的评分机制，该方法力求在技术创新与伦理责任之间取得平衡，从而促进负责任的大语言模型开发。