Skip to Main Content

Artificial Intelligence and Scholarly Research

This guide is designed to help answer questions and provide resources regarding AI, ChatGPT, Large Language Models, and their relationship to academics.

General Resources on Ethics & AI

Bias in Technology & AI

Bias in AI

A key challenge with AI is the potential for bias in the text it produces. Large language models learn from vast amounts of online data and text, and because they are designed to predict the most likely word sequences, they can reflect and even amplify existing biases found in that data. Furthermore, some AI systems use human feedback to refine their responses, but this process can also introduce bias if the human testers are not neutral. Consequently, generative AI like ChatGPT has been shown to produce socio-politically biased content, sometimes including sexist, racist, or offensive material.

Selected Readings on Bias in Technology and LLMs

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), March 3–10, 2021, Virtual Event, Canada.  https://doi.org/10.1145/3442188.3445922.

Browne, Grace. “AI Is Steeped in Big Tech’s ‘Digital Colonialism.’” Wired, May 25, 2023. https://www.wired.com/story/abeba-birhane-ai-datasets/.

Buolamwini, Joy. Unmasking AI: A Story of Hope and Justice in a World of Machines. New York: Random House, 2023.

Glazko, Kate, Yusuf Mohammed, Ben Kosa, Venkatesh Potluri, and Jennifer Mankoff. “Identifying and Improving Disability Bias in GPT-Based Resume Screening.” In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24), June 03–06, 2024, Rio de Janeiro, Brazil. https://doi.org/10.1145/3630106.3658933.

“How Artificial Intelligence Bias Affects Women and People of Color.” UCB-UMT, December 8, 2021. https://ischoolonline.berkeley.edu/blog/artificial-intelligence-bias/.

Lizarraga, Lori. “How Does a Computer Discriminate?” NPR Code Switch, November 8, 2023. https://www.npr.org/2023/11/08/1197954253/how-ai-and-race-interact.

Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: University Press, 2018.

Omiye, Jesutofunmi A., Jenna C. Lester, Simon Spichak, Veronica Rotemberg, and Roxana Daneshjou. “Large Language Models Propagate Race-Based Medicine.” NPJ Digital Medicine 6, no. 1 (2023): 1–4. https://doi.org/10.1038/s41746-023-00939-z.

Selected Readings on Environmental & Human Impacts of AI

AI and the Environment

The environmental cost of increasingly used generative AI, driven by its energy needs (Saenko, 2024), is largely unknown due to industry secrecy. However, concerns are rising that the computing power needed could significantly inflate data centers' energy consumption and carbon footprint (Calma, 2023).

How exactly does generative AI impact the environment?

  • Energy consumption: A huge amount of computing power is needed to create and train generative AI models and to generate its outputs. According to research, "It's estimated that a search driven by generative AI uses four to five times the energy of a conventional web search. Within years, large AI systems are likely to need as much energy as entire nations" (Crawford, 2024). In other words, typing a prompt into ChatGPT results in much more energy use than typing a search query into Google.
  • Water use: The data centers that house generative AI servers consume an enormous amount of energy and resources. Much of this resource consumption consists of the water needed to provide cooling for computer servers. Shaolei Ren, a professor of engineering at UC Riverside, "estimates that a person who engages in a session of questions and answers with GPT-3...drives the consumption of a half-liter of fresh water" (Berreby, 2024). Ren and colleagues suggest that "globally, the demand for water for AI could be half that of the United Kingdom by 2027" (Crawford, 2024).
  • Creating demand for more energy resources: The computing power needed for generative AI has driven tech companies to seek out more energy sources. This has included proposing nuclear energy as a solution (Crawford, 2024) or increasing the consumption of natural gas (Kimball, 2024). These energy demands would further impact an environment already strained by climate change.

Researchers have shown that it is possible to reduce the energy costs of generative AI by using more renewable energy, implementing sustainable construction of data centers, and scheduling computation during certain times of the day (Saenko, 2024). These practices would require transparency and commitment from tech companies and advocacy from users and policymakers.

Selected Readings on the Environmental Impacts of Generative AI

Berreby, David. "As Use of AI Soars, So Does the Energy and Water it Requires." Yale Environment 360, February 6, 2024. https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions.

Calma, Justine. "The Environmental Impact of the AI Revolution is Starting to Come into Focus." The Verge, October 10, 2023. https://www.theverge.com/2023/10/10/23911059/ai-climate-impact-google-openai-chatgpt-energy.

Crawford, Kate. "Generative AI's environmental costs are soaring -- and mostly secret." Nature, February 20, 2024. https://www.nature.com/articles/d41586-024-00478-x.

Saenko, Kate. "A Computer Scientist Breaks Down Generative AI's Hefty Carbon Footprint." Scientific American, May 25, 2023. https://www.scientificamerican.com/article/a-computer-scientist-breaks-down-generative-ais-hefty-carbon-footprint/.

Human Labor & AI

Human Labor and Generative AI

Contrary to the idea of purely machine-generated text, human laborers are essential to chatbots like ChatGPT. They label data, provide feedback for human-like responses, and detect toxic content (Dzieza, 2023). This often exploits underpaid workers in the Global South, a practice some call "digital neocolonialism" (Browne, 2023; Perrigo, 2023).

Selected Readings on Human Labor and Generative AI

Browne, Grace. "AI is Steeped in Big Tech's 'Digital Colonialism.'" Wired, May 25, 2023. https://www.wired.com/story/abeba-birhane-ai-datasets/.

Dzieza, Josh. "AI is a Lot of Work." The Verge, June 20, 2023. https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots.

Perrigo, Billy. "OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic." TIME, January 18, 2023. https://time.com/6247678/openai-chatgpt-kenya-workers/.