From Corporate Greenwashing to Thirsty Data Centers: How Young People Analyze GenAI’s Environmental Costs

• In Event: Ghosts in the AI Machine: Unforgetting the Artificial Histories Haunting Educational Futures

Abstract

Objectives

The environmental costs of AI technologies, such as generative AI (GenAI), include mining rare earth minerals for computer chips, burning fossil fuels to provide electricity to train large language models, and draining aquifers to cool data centers (Crawford, 2021; Hogan, 2024). As the debate of how, if at all, to use GenAI in education continues, one crucial starting point for students is answering a fundamental question: is GenAI ethical?

In my study, I examined how youth as technoskeptical philosophers of technology analyzed an ecology of GenAI. I posed two research questions:

What kinds of critical analysis of generative AI’s ecologies did a technoskeptical inquiry open for students?

What types of relationships informed students’ sensemaking about generative AI while completing a technoskeptical inquiry?

Theoretical Framework

My study applies and modifies the philosophers of technology theoretical framework, which views youths’ “contemplation as a privileged mode of sensemaking with and about technology” (Vakil & McKinney de Royston, 2022, p. 340). I also turn to technoskepticism (Pleasants et al., 2023) to guide my analysis. Both frameworks are concerned with how young people learn to develop justice-oriented relationships with, through, and against technology.

Data and Methods

The study’s data come from the Young People’s Race, Power, and Technology Project (YPRPT), a social design experiment (Gutiérrez & Jurow, 2016) in a Midwestern city. I focused on the fifth and most recent iteration of YPRPT. The six-week program occurred during Summer 2024 in partnership with Digital Youth Network and explored the ethics of AI. The research participants were six high school students, three of whom identified as Black males and three of whom identified as Black females. Students completed a technoethical audit (Symposia authors, 2019) of GenAI–which included an examination of GenAI’s environmental impacts–and then engaged in a 67-minute discussion of the ethics of GenAI.

I produced a transcript of the discussion and completed two rounds of deductive and inductive coding (Miles et al., 2020). The coding process led to five themes: 1) Big Tech’s mechanisms of extraction and exploitation; 2) Corporate “washing” efforts; 3) Singularity and solidarity; 4) Corporate motivation and technology’s impacts; and 5) Accountability. My analysis centers two focal students, Kiara and Stephanie, who drew on GenAI’s climate costs to determine GenAI is unethical.

Results and Significance

My findings suggest designing an expansive ethical terrain for youth to traverse when considering the ethics of GenAI. Kiara constructed a holistic critical analysis of AI guided by a sense of injustice at Big Tech’s abuses at multiple scales. Stephanie connected the dots between technology companies' profit-driven business models and their greenwashing efforts.

Observing the youths’ ethical sensemaking illuminates the importance of engaging young people in AI’s political economy, with a specific emphasis on mapping AI’s environmental impacts across supply chains and product life cycles. Youth are often sold GenAI tools with stories about an inevitable AI-first future. My study challenges technosolutionist narratives and offers an alternative means for realizing environmental justice.

Author

• Charles Logan, Northwestern University