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Paper short abstract

This paper studies AI and Sustainability-related policies to examine the socio-ecological implications of AI infrastructures in India and Mexico. It includes interviews with AI policy stakeholders in India and Mexico to understand their perspectives, rationalities and justifications in the process.

Paper long abstract

Growing concerns surround the increasing energy demands and environmental impacts of resource extraction for AI chips, running of AI data centres, and the training of models (Crawford, 2021; Hodgkingson et al., 2024; Lehuedé, 2025). Scholars have brought focus to difficulties with calculating the environmental costs of AI technologies and greenwashing, and there have been efforts to increase awareness about these impacts (Hao, 2024 and 2025; Heikkilä, 2022). Current scholarship at the intersections of AI, sustainability and policy studies has tended to focus on leveraging AI for achieving sustainability goals (Nishant et al., 2020). However, little is known about the experiences and policies that shape the lives of those most affected by new data infrastructures. This is critical because Global South countries bear a larger share of the brunt of the environmental costs associated with data centre and ICT infrastructure development, while reaping fewer benefits from digitisation (UNCTAD, 2024).

Our paper draws on an ongoing research project that examines the socio-ecological implications of AI infrastructure development in India and Mexico, with a focus on AI policies and lived experiences of communities. In this presentation, we specifically focus on AI and Sustainability-related policies, including international, Indian and Mexican policy documents related to AI technologies, as well as adjacent and associated policies (e.g. land acquisition, water resourcing, environmental clearances and labour laws). This is supplemented by semi-structured interviews (n=10) with AI policy stakeholders each in India and Mexico, to understand their perspectives on the sustainability implications of AI technologies and infrastructures.