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

2026-02-23

AI Summary

This study addresses the environmental and ethical challenges posed by large-scale AI training—namely high energy consumption, substantial carbon emissions, diminishing returns in performance gains, and data bias—by operationalizing the concept of “data frugality” into an empirically viable framework. The authors propose a coreset-based subset selection method that significantly reduces training energy use and carbon footprint while incurring only minimal accuracy degradation and effectively mitigating dataset bias. By quantifying the environmental costs associated with downstream tasks on ImageNet-1K, the work demonstrates the practical feasibility of data frugality and offers actionable guidelines for implementing greener AI practices without compromising model performance.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Sustainability and climate impact of web technologies
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• data frugality
• responsible AI
• environmental impact
• data scaling
• energy consumption

Innovation

• data frugality
• coreset selection
• energy efficiency
• responsible AI
• carbon emissions