Foundation models (e.g. ChatGPT, StableDiffusion) pervasively influence
society, warranting immediate social attention. While the models themselves
garner much attention, to accurately characterize their impact, we must
consider the broader sociotechnical ecosystem. We propose Ecosystem Graphs as a
documentation framework to transparently centralize knowledge of this
ecosystem. Ecosystem Graphs is composed of assets (datasets, models,
applications) linked together by dependencies that indicate technical (e.g. how
Bing relies on GPT-4) and social (e.g. how Microsoft relies on OpenAI)
relationships. To supplement the graph structure, each asset is further
enriched with fine-grained metadata (e.g. the license or training emissions).
We document the ecosystem extensively at
this https URL. As of March 16, 2023, we annotate
262 assets (64 datasets, 128 models, 70 applications) from 63 organizations
linked by 356 dependencies. We show Ecosystem Graphs functions as a powerful
abstraction and interface for achieving the minimum transparency required to
address myriad use cases. Therefore, we envision Ecosystem Graphs will be a
community-maintained resource that provides value to stakeholders spanning AI
researchers, industry professionals, social scientists, auditors and
policymakers.

使用生态图作为记录架构，将数据集、模型和应用程序联结在一起，同时补充详细的元数据，为各种用例提供最小的透明度。