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.

翻译：本文提出了面向伦理AI模型训练的社会数据与知识集体智能平台（STREAM），旨在解决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，以助力AI模型实现“从善如流”。通过构建一个全面且具有代表性的平台，精准反映包括人类与AI在内的多元群体的道德判断，我们希望有效刻画文化与群体差异，捕捉道德判断随时间的动态演变，进而促进AI模型道德能力的建立、评估、嵌入、具身、集成与演化（6Es）。目前，STREAM已提供涵盖广泛伦理场景的全面收集，并积累了由志愿者及多种主流大语言模型（LLMs）共同标注的大量道德判断数据，综合呈现了人类与AI在不同道德情境下的道德偏好与表现。本文将概述STREAM的现有架构与构建过程，探讨其潜在应用，并展望未来发展方向。