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The socio-ecological costs of AI: Toward socially responsible and sustainable communication practices

Emma Christensen

Public Relations Inquiry2025https://doi.org/10.1177/2046147x251377853article
ABDC B
Weight
0.37

What the paper says

The adoption of generative artificial intelligence (GAI) among communication practitioners and researchers surged after the launch of ChatGPT in November 2022. This adoption has long been called for, driven by expectations of efficiency and productivity gains as well as concerns about being outpaced by adjacent fields. As the initial hype around GAI fades, a more balanced understanding of AI’s promises and perils can emerge. While previous research on the perils of AI has emphasized ethical concerns such as job displacement, the potential spread of mis- and disinformation, immoral communication, and intensified social injustice, one critical issue remains largely unaddressed: the socio-ecological harm of AI use. While often perceived as immaterial, AI relies on a vast physical infrastructure, demanding the extraction and depletion of non-renewable resources, consuming extreme amounts of energy and water, and generating significant ‘e-waste’. This paper brings these overlooked costs of AI to the forefront, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.

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https://doi.org/https://doi.org/10.1177/2046147x251377853

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@article{emma2025,
  title        = {{The socio-ecological costs of AI: Toward socially responsible and sustainable communication practices}},
  author       = {Emma Christensen},
  journal      = {Public Relations Inquiry},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/2046147x251377853},
}

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The socio-ecological costs of AI: Toward socially responsible and sustainable communication practices

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.