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arXiv · 2606.19957

Modest, artistic, and radical solutions to the environmental impact of image-generating machine learning

Laura U. Marks·Jess MacCormack·Kehui Li
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Abstract

Machine learning is often touted to improve the efficiency of ICT, but that small gain is overwhelmed by the enormous carbon, water, and land footprints of data centers and ML-ready devices. We survey the electricity consumption of ML applications in training and inference, focusing on electricity-intensive image generation. Our team of a computer engineer, a media scholar, and an artist explore solutions including inexact computing; tiny language models; low-precision hardware architectures; hardware with limited capacity; and anticipating and mitigating energy demands at the design phase. We will sketch our work in progress of an ethical and aesthetically sophisticated tiny image generator using non-scraped data. Looking to the economic context, we will propose a true-cost accounting for the environmental impact of machine learning and suggest that the criterion of efficiency is driven by the shareholder-capitalist framing of ICT.

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Laura U. Marks, Jess MacCormack, Kehui Li. 2026-06-18. Modest, artistic, and radical solutions to the environmental impact of image-generating machine learning. https://arxiv.org/abs/2606.19957

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