The mounting human and environmental costs of generative AI

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Dr. Sasha Luccioni is a Researcher and Climate Lead at Hugging Face, where she studies the ethical and societal impacts of AI models and datasets. She is also a director of Women in Machine Learning (WiML), a founding member of Climate Change AI (CCAI), and chair of the NeurIPS Code of Ethics committee. The opinions in this piece do not necessarily reflect the views of Ars Technica.

Over the past few months, the field of artificial intelligence has seen rapid growth, with wave after wave of new models like Dall-E and GPT-4 emerging one after another. Every week brings the promise of new and exciting models, products, and tools. It’s easy to get swept up in the waves of hype, but these shiny capabilities come at a real cost to society and the planet.

Downsides include the environmental toll of mining rare minerals, the human costs of the labor-intensive process of data annotation, and the escalating financial investment required to train AI models as they incorporate more parameters.