Responsible Data Stewardship: Generative AI and the Digital Waste Problem
- URL: http://arxiv.org/abs/2505.21720v1
- Date: Tue, 27 May 2025 20:07:22 GMT
- Title: Responsible Data Stewardship: Generative AI and the Digital Waste Problem
- Authors: Vanessa Utz,
- Abstract summary: generative AI systems enable unprecedented creation levels of synthetic data across text, images, audio, and video modalities.<n>Digital waste refers to stored data that consumes resources without serving a specific (and/or immediate) purpose.<n>This paper introduces digital waste as an ethical imperative within (generative) AI development, positioning environmental sustainability as core for responsible innovation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- The Hidden Costs of AI: A Review of Energy, E-Waste, and Inequality in Model Development [0.0]
Review explores four critical areas where AI's impact extends beyond performance.<n>High emissions from model training, rising hardware turnover, global infrastructure disparities are highlighted.<n>Ultimately, it argues that AI's progress must align with ethical responsibility and environmental stewardship.
arXiv Detail & Related papers (2025-07-13T12:31:42Z) - Rethinking Data Protection in the (Generative) Artificial Intelligence Era [115.71019708491386]
We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
arXiv Detail & Related papers (2025-07-03T02:45:51Z) - Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences [50.9036832382286]
We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
arXiv Detail & Related papers (2025-05-22T12:52:34Z) - Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.<n>We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics.<n>Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
arXiv Detail & Related papers (2025-04-29T10:21:40Z) - Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
arXiv Detail & Related papers (2025-04-01T13:53:11Z) - Data Ecofeminism [0.0]
Generative Artificial Intelligence (GenAI) is driving significant environmental impacts.<n>The paper calls for an urgent reassessment of the GenAI innovation race.
arXiv Detail & Related papers (2025-02-16T11:47:50Z) - Data and System Perspectives of Sustainable Artificial Intelligence [43.21672481390316]
Sustainable AI is a subfield of AI for aiming to reduce environmental impact and achieve sustainability.<n>In this article, we discuss current issues, opportunities and example solutions for addressing these issues.
arXiv Detail & Related papers (2025-01-13T17:04:23Z) - Towards Environmentally Equitable AI [23.332350246411124]
We advocate environmental equity as a priority for the management of future AI systems.<n>We uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions.<n>We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI's environmental inequity.
arXiv Detail & Related papers (2024-12-21T08:46:19Z) - When AI Eats Itself: On the Caveats of AI Autophagy [18.641925577551557]
The AI autophagy phenomenon suggests a future where generative AI systems may increasingly consume their own outputs without discernment.
This study examines the existing literature, delving into the consequences of AI autophagy, analyzing the associated risks, and exploring strategies to mitigate its impact.
arXiv Detail & Related papers (2024-05-15T13:50:23Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Causal Reasoning: Charting a Revolutionary Course for Next-Generation
AI-Native Wireless Networks [63.246437631458356]
Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native.
This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning.
We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
arXiv Detail & Related papers (2023-09-23T00:05:39Z) - Towards Sustainable Artificial Intelligence: An Overview of
Environmental Protection Uses and Issues [0.0]
This paper describes the paradox of an energy-consuming technology serving the ecological challenges of tomorrow.
It draws on numerous examples from AI for Green players to present use cases and concrete examples.
The environmental dimension is part of the broader ethical problem of AI, and addressing it is crucial for ensuring the sustainability of AI in the long term.
arXiv Detail & Related papers (2022-12-22T14:31:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.