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

STREAM: Social data and knowledge collective intelligence platform for  TRaining Ethical AI Models

Social platforms have emerged as a crucial platform for disseminating and
discussing information about real-life events, which offers an excellent
opportunity for early detection of newsworthy events. However, most existing
approaches for event detection solely exploit keyword burstiness or network
structures to detect hot events. Thus, they often fail to identify emerging
social events before reaching a trending state regarding the challenging nature
of events and social data. Social data, e.g., tweets, is characterized by
misspellings, incompleteness, ambiguity, and irregular language, as well as
variation in aspects of opinions. Moreover, learning the evolving
characteristics of the events utilizing limited contextual knowledge is almost
infeasible for machine learning models. To address these problems, in this
paper, we propose a framework that exploits the lexical, semantic, and
contextual representations of streaming social data. In particular, we leverage
contextual knowledge to detect semantically related tweets in their earliest
emergence and enhance the quality of produced clusters. We next produce a
cluster chains for each event to show the evolving variation of the event
through time. We conducted extensive experiments to evaluate our framework,
validating the effectiveness of the proposed framework in detecting and
distinguishing social events.

社交平台是传播和讨论真实事件信息的关键平台，本研究提出了一种利用社交数据的词汇、语义和上下文表示来检测社交事件的框架，通过利用上下文知识在最早的阶段检测语义相关的推文并提高聚类质量，进而展示每个事件的演变变化。经过广泛实验验证了该框架在检测和区分社交事件方面的有效性。