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

TL;DR

STREAM is a platform that collects diverse moral judgments from humans and AI to help train ethical AI models aligned with human values and cultural variations.

Contribution

The paper introduces STREAM, a comprehensive platform for gathering and utilizing moral judgment data to improve ethical AI training and evaluation.

Findings

Collected extensive ethical scenarios and moral judgments from humans and AIs.

Demonstrated the platform's ability to reflect cultural and group moral variations.

Showcased potential applications in training and assessing ethical AI models.

Abstract

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.