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Using Generative Art to Convey Past and Future Climate Transitions

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Generative artificial intelligence and sustainability - The challenges.

David Woodruff Thomforde^1,2

¹Western Michigan University, Kalamazoo, MI, USA (retired).

Work (Reading, Mass.)

|July 4, 2025

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) offers significant productivity gains but raises concerns regarding resource use, environmental impact, and cultural bias. Addressing these challenges is crucial for equitable AI development and deployment.

Keywords:

artificial intelligence creativity damage environmental justice production sustainable

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Area of Science:

Computer Science
Artificial Intelligence
Ethics

Background:

Generative artificial intelligence (AI) presents transformative potential across diverse sectors.
However, its rapid advancement introduces significant challenges.
These include the substantial consumption of scarce resources and environmental sustainability concerns.

Purpose of the Study:

To examine the dual nature of generative AI: its capacity for productivity enhancement.
To identify and analyze the inherent challenges associated with its widespread adoption.
To highlight the critical issues of resource intensity, sustainability, and potential biases.

Main Methods:

Literature review of current generative AI applications and their impacts.
Analysis of resource consumption data and environmental footprints.
Examination of algorithmic bias and its societal implications, particularly concerning marginalized groups.

Main Results:

Generative AI significantly boosts productivity in various fields.
The technology demands considerable energy and computational resources, impacting sustainability.
A notable risk of bias exists, potentially favoring Western cultural perspectives and disadvantaging marginalized communities.

Conclusions:

Generative AI's benefits are substantial but contingent on responsible development.
Mitigating resource use and environmental impact is essential for sustainable AI.
Proactive measures are required to address and counteract inherent biases for equitable outcomes.