Work

How CO2STLY Is CHI? The Carbon Footprint of Generative AI in HCI Research and What We Should Do About It

Year: 2025

Type: conference-paper

Abstract: The energy cost of developing and deploying Generative AI (GenAI) models has exploded with their mass adoption, as has the ensuing carbon emissions. The climate impact of this is currently unknown. In Human-Computer Interaction, GenAI models are rarely trained but often used. Based on detailed review of 282 papers, we estimate this footprint from energy consumption of the total use of GenAI for CHI 2024 research as between 10,769.63 and 10,925.12 kg CO2e - equal to driving a car for more than 100,000 km. We show that in CHI research, GenAI is most often used for Prototyping, Evaluation & User studies, and that Data Collection and Fine-tuning models incurs the highest CO2st.1 We find that CHI submissions are unlikely to report GenAI use transparently, which makes precise calculations difficult. By measuring the usage of a subset of the papers on local hardware, we obtain estimations of the energy consumption and carbon footprint. Based on this evidence, we discuss and demonstrate ways to mitigate the issues of GenAI carbon footprint and lack of transparency.

Source: CHI Conference on Human Factors in Computing Systems

DOI: 10.1145/3706598.3714227

Authors: Nanna Inie, Jeanette Falk, Raghavendra Selvan

Institutions: IT University of Copenhagen, Aalborg University, University of Copenhagen

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Funder: Villum Fonden