This semester we covered a lot when it comes to data collection, privacy, the environment, bias in generative AI, and so much more. Covering these vital topics in regard to generative AI has recently made me pose a question to myself: what are some concerns I am going to face when doing this digital research project?

To add more context, my DH project is on the representation of Black athletes vs. white athletes during the civil rights period. I had to narrow it down to a more specific question: “How accurately do LLMs represent well-known Black athletes compared to well-known white athletes from 1954 to 1968, the years of the civil rights movement, and was either group’s representation minimized or distorted because of that time period?”

When I reflected on everything we have read, the first concern that came to mind was the environmental impact this project is going to have. When reading “Explained: Generative AI’s Environmental Impact,” I became aware of how much generative AI is damaging the environment. Adam Zewe reports that “the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI” (Zewe). That is almost double in just one year. My project alone won’t use anywhere near that much, but since I am using three different LLMs to get information on the same athletes, every prompt I send adds to that demand. Each token you submit to an LLM uses resources from the earth. I feel a bit of cognitive dissonance because I know this has a cost, but I am still going to do it so I can test which LLMs spit out fake sources and which give us actual information. To counteract this, I will refine my prompts for each LLM so I can limit the amount of harm I’m doing to the environment.

My project is also about who gets left out, which connects to inclusive design. In an article on inclusive design principles, Carla Gonzalez Cidoncha writes that “digital products are often designed with a narrow definition of the ‘typical’ user in mind” (Gonzalez Cidoncha). If the people and data behind an LLM have a narrow idea of whose history matters, then the tool might give detailed answers about white athletes and thin or wrong answers about Black athletes. That is exactly what I want to find out.

The other obstacle I might face is finding information on these athletes. Even though UA has a large archive, right now I can’t find anything but newspaper coverage of events that happened, with no real information about the players’ careers. Because of this, I told myself not to limit myself to sources from UA.

Works Cited

Gonzalez Cidoncha, Carla. “Inclusive Design Principles: How to Build Digital Experiences That Work for Everyone.” iubenda, 19 Mar. 2026, www.iubenda.com/en/help/181591-inclusive-design.

Zewe, Adam. “Explained: Generative AI’s Environmental Impact.” MIT News, Massachusetts Institute of Technology, 17 Jan. 2025, news.mit.edu/2025/explained-generative-ai-environmental-impact-0117.