Ecosystem Graphs: The Social Footprint of Foundation Models (2303.15772v1)

Abstract: 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 https://crfm.stanford.edu/ecosystem-graphs/. 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.

• The paper introduces Ecosystem Graphs to document and enhance transparency in foundation model networks by mapping dependencies and metadata.
• It details a framework capturing 262 nodes and 356 dependencies across datasets, models, and applications to reveal interconnectivity.
• The study suggests that these graphs guide developers, researchers, and policymakers in fostering robust AI governance and responsible deployment.

Analyzing the Social Footprint of Foundation Models through Ecosystem Graphs

The paper "Ecosystem Graphs: The Social Footprint of Foundation Models" by Bommasani et al. offers a compelling framework for understanding the extensive sociotechnical networks formed by foundational models such as ChatGPT and Stable Diffusion. This is critical because, despite their transformative capabilities, there is significant opacity regarding their deployment and societal ramifications. The authors propose Ecosystem Graphs as a centralized knowledge documentation framework aimed at increasing transparency in the foundation model ecosystem by mapping dependencies and assets with enriched metadata.

Framework Implementation and Numerical Insights

The paper details the construction of Ecosystem Graphs, comprising assets categorized as datasets, models, and applications, linked through defined dependencies. Each component is accompanied by metadata, augmenting transparency around technical and social interrelationships. As of March 16, 2023, the graph includes 262 nodes comprising 64 datasets, 128 models, and 70 applications, interconnected by 356 dependencies, and annotated through 3850 metadata entries. This expansive documentation effort spans 63 organizations across nine modalities, highlighting the centrality of key assets such as The Pile dataset and the PaLM model.

Ecosystem Graphs also serve as a tool for analyzing the hubs within these networks. For instance, it shows the pervasive reliance on EleutherAI's The Pile by numerous organizations. For developers, hubs can signify high-impact areas; for policymakers and auditors, they pinpoint focal points for scrutiny and risk assessment, offering a roadmap to understand how resources and influence flow within the ecosystem.

Implications and Future Directions

The Ecosystem Graphs framework emphasizes the importance of transparency and governance in advancing the responsible deployment of foundation models. By offering insights into the interdependencies and sociopolitical dynamics of AI technologies, Ecosystem Graphs can guide stakeholders—from developers and researchers to policymakers and auditors—in their decision-making processes. The necessity for continuous maintenance and potential policy incorporation is underscored by the evolving nature of the ecosystem, where models and practices rapidly change.

With increasing reliance on foundational models, ensuring the ecosystem's transparency becomes necessary for minimizing potential harms. The paper suggests that this initiative could lead to more robust industry standards and policies, mirroring practices like supply chain tracking in other domains. Looking ahead, the authors speculate on decentralized community contributions and incentivization strategies to sustain this transparency framework, potentially evolving into a broad-use public repository that remains diligent against the backdrop of technological progression and societal impact.

Conclusion

In summary, the paper presents Ecosystem Graphs as an innovative approach to demystify the intricate network constituting the foundation model landscape. This contribution is not only of theoretical significance but also of practical utility, as it addresses the intricate details required to establish transparency, accountability, and governance in the rapidly evolving field of AI. By systematically documenting and analyzing the dependencies within the AI ecosystem, this framework lays the groundwork for a more transparent, inclusive, and socially responsible future for AI technologies.