How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI

TL;DR

This paper investigates the environmental, social, and economic costs of large-scale data in frontier AI, highlighting how hyper-datafication redistributes burdens and proposing recommendations to mitigate these impacts.

Contribution

It introduces the concept of hyper-datafication, analyzes extensive datasets and qualitative responses, and offers Data PROOFS guidelines to address data-related sustainability costs in AI.

Findings

Hyper-datafication increases resource consumption and environmental burdens.

Data-related costs are disproportionately shifted to the Global South and under-represented groups.

Proposed Data PROOFS recommendations aim to mitigate sustainability costs.

Abstract

Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology corporations to aggregate and curate internet-scale datasets. In this work, we examine the environmental, social, and economic costs of large-scale data in AI through a sustainability lens. We argue that the field is shifting from building models from data to actively creating data for building models. We characterise this transition as hyper-datafication, which marks a critical juncture for the future of frontier AI and its societal impacts. To quantify and contextualise data-related costs, we analyse approximately 550,000 datasets from the Hugging Face Hub, focusing on dataset growth, storage-related energy consumption and carbon footprint, and societal representation using language data. We complement this analysis with qualitative responses from data workers in Kenya to examine the labour involved, including direct employment by big tech corporations and exposure to graphic content. We further draw on external data sources to substantiate our findings by illustrating the global disparity in data centre infrastructure. Our analyses reveal that hyper-datafication does not merely increase resource consumption but systematically redistributes environmental burdens, labour risks, and representational harms toward the Global South, precarious data workers, and under-represented cultures. Thus, we propose Data PROOFS recommendations spanning provenance, resource awareness, ownership, openness, frugality, and standards to mitigate these costs. Our work aims to make visible the often-overlooked costs of data that underpin frontier AI and to stimulate broader debate within the research community and beyond.