Ecosystem Graphs: Documenting the Foundation Model Supply Chain

Foundation models (e.g. GPT-4, Gemini, Llama 3) pervasively influence society, warranting greater understanding. While the models garner much attention, accurately characterizing their impact requires considering the broader sociotechnical ecosystem in which they are created and deployed. We propose Ecosystem Graphs as a documentation framework to centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical and social relationships. To supplement the graph structure, each asset is further enriched with fine-grained metadata, such as the model’s estimated training emissions or licensing guidelines. Since its release in March 2023, Ecosystem Graphs represents an ongoing effort to document 568 assets (112 datasets, 359 models, 97 applications) from 117 organizations. Ecosystem Graphs functions as a multifunctional resource: we discuss two major uses by the 2024 AI Index and the UK’s Competition and Markets Authority that demonstrate the value of Ecosystem Graphs.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31629
Citations
1
Primary Topic
Microbial Community Ecology and Physiology
Type
article
Field-Weighted Citation Impact
0.46

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article

Ecosystem Graphs: Documenting the Foundation Model Supply Chain

Percy Liang, Kathleen Creel, Thomas I. Liao, Rishi Bommasani et al.
1 citations
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Microbial Community Ecology and Physiology
0.46
article

Ecosystem Graphs: Documenting the Foundation Model Supply Chain

Percy Liang, Kathleen Creel, Thomas I. Liao, Rishi Bommasani, Dilara Soylu
article en
1 citations

Abstract

Foundation models (e.g. GPT-4, Gemini, Llama 3) pervasively influence society, warranting greater understanding. While the models garner much attention, accurately characterizing their impact requires considering the broader sociotechnical ecosystem in which they are created and deployed. We propose Ecosystem Graphs as a documentation framework to centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical and social relationships. To supplement the graph structure, each asset is further enriched with fine-grained metadata, such as the model’s estimated training emissions or licensing guidelines. Since its release in March 2023, Ecosystem Graphs represents an ongoing effort to document 568 assets (112 datasets, 359 models, 97 applications) from 117 organizations. Ecosystem Graphs functions as a multifunctional resource: we discuss two major uses by the 2024 AI Index and the UK’s Competition and Markets Authority that demonstrate the value of Ecosystem Graphs.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Universidad del Noreste (MX), Stanford University (US)
National Science Foundation
Openalex Percentile: Top 28%
Microbial Community Ecology and Physiology
0.46
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Ecosystem Graphs: Documenting the Foundation Model Supply Chain — Percy Liang, Kathleen Creel, et al. · Proceedings of the AAAI/ACM Conference on AI Ethics and Society (2024) | TGRS Research Map | TGRS