Stop Preaching and Start Practising Data Frugality for Responsible Development of AI

TL;DR

This paper advocates for adopting data frugality in AI development, emphasizing environmental sustainability and efficiency, supported by empirical evidence showing that subset selection can reduce energy use with minimal accuracy loss.

Contribution

It highlights the environmental costs of data scaling and demonstrates practical benefits of data frugality through empirical experiments on subset selection methods.

Findings

Coreset-based subset selection reduces training energy consumption.

Data frugality can mitigate dataset bias.

Large-scale data growth yields diminishing returns in performance.

Abstract

This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development. For long, progress has been equated with ever-larger datasets, driving remarkable advances but now yielding increasingly diminishing performance gains alongside rising energy use and carbon emissions. While awareness of data frugal approaches has grown, their adoption has remained rhetorical, and data scaling continues to dominate development practice. We argue that this gap between preach and practice must be closed, as continued data scaling entails substantial and under-accounted environmental impacts. To ground our position, we provide indicative estimates of the energy use and carbon emissions associated with the downstream use of ImageNet-1K. We then present empirical evidence that data frugality is both practical and beneficial, demonstrating that coreset-based subset selection can substantially reduce training energy consumption with little loss in accuracy, while also mitigating dataset bias. Finally, we outline actionable recommendations for moving data frugality from rhetorical preach to concrete practice for responsible development of AI.