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.

该论文介绍了用于培训具有伦理人工智能模型的社会数据和知识集体智能平台（STREAM），以解决将人工智能模型与人类道德价值观对齐的挑战，并提供道德数据集和知识库，以帮助推广人工智能模型“像溪流自然而然地遵循好的建议”。通过创建一个全面而代表性的平台，准确地反映了包括人类和人工智能在内的不同群体的道德判断，我们希望有效地描绘文化和群体的变化，并捕捉到道德判断随时间的动态演变，从而促进人工智能模型道德能力的建立、评估、嵌入、体现、集成和发展（6Es）。目前，STREAM已经提供了一套全面的道德场景，并收集了志愿者和各种流行的大型语言模型（LLMs）注释的大量道德判断数据，共同展示了人类和人工智能在各种道德背景下的道德偏好和表现。本文将概述STREAM的当前结构和构建，探讨其潜在应用，并讨论其未来前景。

STREAM：社交数据和知识集体智能平台，用于训练道德AI模型