Abstract:

The talk will focus on the paper "Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives", accepted as a Spotlight Position Paper at ICML 2026. 

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. The authors 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. They argue that sustainable open-source AI requires coordination infrastructure that tracks impacts across model lineages, not only base models. They 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.

Bio:
Dr. Shaina Raza is an Applied Machine Learning Scientist in Responsible AI at the Vector Institute for Artificial Intelligence. She holds a PhD in Computer Science and completed her postdoctoral training at the University of Toronto. With over a decade of experience spanning academia and industry, her research focuses on responsible and equitable AI, large language models, multimodal machine learning, recommender systems, and health informatics. Dr. Raza leads Responsible AI initiatives and collaborates with universities, industry partners, and international consortia, including projects funded through Horizon Europe, CIFAR Canada, and related initiatives . Her work addresses fairness, bias mitigation, disinformation detection, and trustworthy AI, with applications in healthcare, social media, and public policy.