STREAM：用于训练伦理AI模型的社会数据与知识集体智能平台
STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
AI总结：
本文提出用于训练伦理AI模型的集体智能平台STREAM，以应对AI与人类道德价值观对齐难题，目前已积累多源标注的伦理场景与判断数据，将推动AI道德能力的6E建设。
摘要：
本文提出了用于训练伦理AI模型的社会数据与知识集体智能平台（STREAM，Social data and knowledge collective intelligence platform for TRaining Ethical AI Models），以应对AI模型与人类道德价值观对齐的挑战，并提供伦理数据集与知识库，助力推动AI模型“如水流顺道般自然地遵从良善建议”。通过构建一个全面且具代表性、能准确反映包括人类和AI在内的多元群体道德判断的平台，我们希望有效呈现文化与群体差异，并捕捉道德判断随时间的动态演变，进而推动AI模型道德能力的“6E”建设：确立（Establishment）、评估（Evaluation）、嵌入（Embedding）、具身（Embodiment）、集成（Ensemble）与演化（Evolvement）。目前，STREAM已收录全面的伦理场景集合，并积累了由志愿者及各类主流大语言模型（LLMs，Large Language Models）标注的大量道德判断数据，共同呈现了人类与AI在一系列道德情境下的道德偏好与表现。本文将概述STREAM的当前结构与构建情况，探讨其潜在应用，并展望其未来前景。
英文摘要：
This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course". By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models (LLMs), collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
