This paper explores how AI-owners can develop safeguards for AI-generated content by drawing from established codes of conduct and ethical standards in other content-creation industries. It delves into the current state of ethical awareness on Large Language Models (LLMs). By dissecting the mechanism of content generation by LLMs, four key areas (upstream/downstream and at user prompt/answer), where safeguards could be effectively applied, are identified. A comparative analysis of these four areas follows and includes an evaluation of the existing ethical safeguards in terms of cost, effectiveness, and alignment with established industry practices. The paper's key argument is that existing IT-related ethical codes, while adequate for traditional IT engineering, are inadequate for the challenges posed by LLM-based content generation. Drawing from established practices within journalism, we propose potential standards for businesses involved in distributing and selling LLM-generated content. Finally, potential conflicts of interest between dataset curation at upstream and ethical benchmarking downstream are highlighted to underscore the need for a broader evaluation beyond mere output. This study prompts a nuanced conversation around ethical implications in this rapidly evolving field of content generation.

翻译：本文探讨了AI所有者如何借鉴其他内容创作行业中既有的行为准则和伦理标准，为AI生成内容建立安全保障机制。研究深入剖析了大语言模型（LLMs）的伦理认知现状，通过解析LLM的内容生成机制，识别出可有效应用安全措施的四个关键领域（上游/下游环节及用户提示/回答阶段）。随后对这四大领域展开比较分析，评估现有伦理安全措施在成本、有效性及与既有行业实践契合度方面的表现。本文的核心论点是：现有IT相关伦理准则虽适用于传统IT工程，但不足以应对基于LLM的内容生成带来的挑战。借鉴新闻业的成熟实践，我们为参与LLM生成内容分发与销售的企业提出潜在标准框架。最后，通过揭示上游数据集构建与下游伦理基准测试之间的潜在利益冲突，强调需要超越单纯输出评估的更广泛评价体系。本研究旨在推动这一快速演进的内容生成领域开展更为精细的伦理影响对话。