Responsible Data Stewardship: Generative AI and the Digital Waste Problem

TL;DR

This paper highlights the overlooked issue of digital waste in generative AI, emphasizing its environmental impact and proposing strategies for sustainable data management and ethical AI development.

Contribution

It introduces digital waste as a new ethical concern in AI, drawing from other disciplines to suggest mitigation approaches and expanding AI ethics to include environmental sustainability.

Findings

Digital waste is a significant but understudied sustainability challenge.

Transferable digital resource management strategies can be adapted for AI.

Recommendations for technical, research, and cultural interventions are proposed.

Abstract

As generative AI systems become widely adopted, they enable unprecedented creation levels of synthetic data across text, images, audio, and video modalities. While research has addressed the energy consumption of model training and inference, a critical sustainability challenge remains understudied: digital waste. This term refers to stored data that consumes resources without serving a specific (and/or immediate) purpose. This paper presents this terminology in the AI context and introduces digital waste as an ethical imperative within (generative) AI development, positioning environmental sustainability as core for responsible innovation. Drawing from established digital resource management approaches, we examine how other disciplines manage digital waste and identify transferable approaches for the AI community. We propose specific recommendations encompassing re-search directions, technical interventions, and cultural shifts to mitigate the environmental consequences of in-definite data storage. By expanding AI ethics beyond immediate concerns like bias and privacy to include inter-generational environmental justice, this work contributes to a more comprehensive ethical framework that considers the complete lifecycle impact of generative AI systems.