🤖 AI Summary
This work addresses the lack of a unified mechanism for tracking the environmental impact of open-source AI model derivatives—such as fine-tuned, quantized, or merged variants—rendering their energy use, water consumption, and carbon emissions largely invisible. To bridge this gap, the paper introduces the Data and Impact Accounting (DIA) framework, which extends environmental footprint tracking from base models to the entire lineage of derived models for the first time. DIA employs a lightweight, transparent layer to standardize carbon and water footprint metadata, integrates low-overhead measurement tools, and visualizes cumulative impacts through a public dashboard. By enabling comparability across model versions and lineages, DIA provides an unobtrusive, scalable infrastructure for sustainability accountability within the open-source AI ecosystem.
📝 Abstract
Open-source AI is scaling rapidly, and model hubs now host millions of artifacts. Each foundation model can spawn large numbers of fine-tunes, adapters, quantizations, merges, and forks. We take the position that compute efficiency alone is insufficient for sustainability in open-source AI: lower per-run costs can accelerate experimentation and deployment, increasing aggregate environmental footprint unless impacts are measurable and comparable across derivative lineages. However, the energy use, water consumption, and emissions of these derivative lineages are rarely measured or disclosed in a consistent, comparable manner, leaving ecosystem-level impact largely invisible. We argue that sustainable open-source AI requires coordination infrastructure that tracks impacts across model lineages, not only base models. We propose Data and Impact Accounting (DIA), a lightweight, non-restrictive transparency layer that (i) standardizes carbon and water reporting metadata, (ii) integrates low-friction measurement into common training and inference pipelines, and (iii) aggregates reports through public dashboards to summarize cumulative impacts across releases and derivatives. DIA makes derivative costs visible and supports ecosystem-level accountability while preserving openness. https://vectorinstitute.github.io/ai-impact-accounting/