  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.
