Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives

Shaina Raza, Iuliia Zarubiieva, Ahmed Y. Radwan, Nathaniel Lesperance, Deval Pandya, Sedef Akinli Kocak, Graham W. Taylor
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:171864-171879, 2026.

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 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 way, leaving aggregate ecosystem impact largely invisible. We argue that sustainable open-source AI requires a 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 via public dashboards to summarize cumulative impacts across releases and derivatives. DIA makes derivative costs visible and supports ecosystem-level accountability while preserving openness. Project Page: https://vectorinstitute.github.io/ai-impact-accounting

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-raza26a, title = {Position: Sustainable Open-Source {AI} Requires Tracking the Cumulative Footprint of Derivatives}, author = {Raza, Shaina and Zarubiieva, Iuliia and Radwan, Ahmed Y. and Lesperance, Nathaniel and Pandya, Deval and Akinli Kocak, Sedef and Taylor, Graham W.}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {171864--171879}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/raza26a/raza26a.pdf}, url = {https://proceedings.mlr.press/v306/raza26a.html}, 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 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 way, leaving aggregate ecosystem impact largely invisible. We argue that sustainable open-source AI requires a 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 via public dashboards to summarize cumulative impacts across releases and derivatives. DIA makes derivative costs visible and supports ecosystem-level accountability while preserving openness. Project Page: https://vectorinstitute.github.io/ai-impact-accounting} }
Endnote
%0 Conference Paper %T Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives %A Shaina Raza %A Iuliia Zarubiieva %A Ahmed Y. Radwan %A Nathaniel Lesperance %A Deval Pandya %A Sedef Akinli Kocak %A Graham W. Taylor %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-raza26a %I PMLR %P 171864--171879 %U https://proceedings.mlr.press/v306/raza26a.html %V 306 %X 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 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 way, leaving aggregate ecosystem impact largely invisible. We argue that sustainable open-source AI requires a 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 via public dashboards to summarize cumulative impacts across releases and derivatives. DIA makes derivative costs visible and supports ecosystem-level accountability while preserving openness. Project Page: https://vectorinstitute.github.io/ai-impact-accounting
APA
Raza, S., Zarubiieva, I., Radwan, A.Y., Lesperance, N., Pandya, D., Akinli Kocak, S. & Taylor, G.W.. (2026). Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:171864-171879 Available from https://proceedings.mlr.press/v306/raza26a.html.

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