Laura Aull, professor of English and linguistics and recent director of the English Department Writing Program at the University of Michigan, joins the Michigan Minds podcast to discuss \"AI English,\" how generative tools disrupt the natural diversity of human speech and why automated hiring and housing systems inherit these linguistic biases. She also explores a largely unexamined downside to our rapid adoption of large language models: the massive, real-world carbon footprint generated by our daily digital conversations.
Do you think people will care whether a human or a robot wrote the news or books we read?
So far, humans care very much. And today I think we should care. And here are three reasons why I think we should. One is the environmental cost of the computer processing used with AI. Estimates suggest that if you pose a simple query to AI to ChatGPT, let's say versus to a search engine like Google, you're using 50 times the electricity for the simple query. I'm a serious scuba diver, and I know that the ocean should not get any warmer, and so I don't think we should normalize that kind of environmental impact.
A second reason is bias. It's clear that AI English tools are biased against language too close to itself or too far away, so it penalizes some English users, including nonnative writers of English, who are more often flagged for plagiarism or more often flagged by AI detection tools even when they haven't used it. And those who use non-standardized dialect AI English don't know varieties from Jamaican English to Indian English.
That leads to the third and final reason, which is that human and AI English are different. Human English has more variation and readability than AI English, meaning that human English has more varied kinds of dialect and genres and it is less grammatically dense and it's more humble. AI English is more formal, more impersonal, and more sure of itself.