Why AI Ethics is unique in higher education
Higher education is set up on its own to address the ethical considerations of AI. This is because AI adoption is already popular in academia.
At the University of Miami in Ohio, “there are courses on AI, there are courses that use AI,” says Vice President of IT Services and CIO David Seidl. As AI uses grow, universities and universities need to give students a “ethical foundation, a conceptual foundation for preparing for the future.”
Many schools have the campus institutional expertise needed to lay the foundations. “We have very thoughtful people. We bring subject expertise from many lenses, so we can have an informed conversation about AI ethics.”
Given the access and use of technology in higher education, and the staffing of experts at these institutions, this scene is already set for conversations about the ethical difficulties of AI. These debates have already begun in many universities.
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Important ethical concerns surrounding AI in academia
At UC San Diego, CIO Vinceckelen says that the biggest ethical issue of AI is democratization. Specifically, the ability to access AI through an intellectual lens.
“People who make important inferences when using AI will benefit more,” he says. “People who can't make less profits.”
The university has ethical mandate to teach critical thinking skills, which continues with concern about the accuracy of AI.
For example, you could ask AI, how do you put cheese on pizza? “And they say, 'glue is the perfect way to keep cheese in pizza,'' says Seidl. “It's about giving the answer to individuals who may or may not be good at assessing the quality of their responses.”
Privacy is a high for Michael Butcher, vice president of student affairs and dean of students at Georgia Coastal University, and also co-chairs the AI Task Force.
“People still don't fully understand what happens when they enter their data into an AI application that is institutionally supported or not institutionally supported,” he says. From personal information to sensitive research, privacy is an important ethical consideration given the nature of academic data.
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Bias is another concern. Asking AI to create nurse photos could portray women because they are trained with data that reflects “long-term biases that exist in society.” “What are we carelessly doing by keeping those things perpetuated by AI?”
There are also questions about academic integrity and the risk that users may lean deeper into AI. Higher education needs to consider “where legitimate academic aid ends and unethical dependence begins,” Butcher says.
Given the various ethical grey regions, higher education is being challenged to establish guardrails early in AI adoption.
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