Ethics of AI in higher education: how schools take place

Why the ethics of AI is unique in higher education

Higher education is only well placed to deal with the ethical considerations of AI, in part because The adoption of AI is already widespread in the academic world.

HAS Miami University In Ohio, “there are courses on AI, and there are courses that use AI,” explains the vice-president of IT services and David SEIDL. As the use of AI widens, colleges and universities must give students “an ethical basis, a conceptual basis to prepare them for the future,” he said.

Many schools have institutional expertise on the campus necessary to file this base. “We have people who are very thoughtful, who bring expertise in terms of many goals, so that you can have well -informed conversations on AI ethics,” explains Tom Andriola, University of California, IrvineVice-Chancellor for IT and data.

Given the access and use of technology through higher education, and the experts endowed in these establishments, the scene is already ready for conversations on the ethical dilemmas of AI. In many colleges and universities, these discussions have already started.

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Key ethical concerns surrounding AI in the academic world

HAS UC San DiegoCIO Vince Kellen says that the first ethical problem with AI is democratization; More specifically, the ability to access AI via an intellectual lens.

“Those who exercise critical reasoning in the use of AI benefit from a greater advantage,” he says. “Those who do not get a lesser advantage.”

Universities have an ethical imperative to teach skills in critical thinking, and this is the concerns about the precision of AI.

For example, you can ask for AI, how do you keep cheese on pizza? “And he says:” glue is a great way to keep cheese on pizza, “says Seidl. “The ethical concern is by giving this response to people who may or may not be gifted to assess the quality of this response.”

Private life ranks Michael Butcher, assistant vice-president of student and dean of students at Coastal Georgia Collegewhere he also copies the AI ​​working group.

“People do not yet fully understand what is happening when they enter their data in a sustained or non-institutional AI application,” he said. Given the nature of academic data – personal information to sensitive research – confidentiality becomes an important ethical consideration.

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Another concern is bias. Ask the AI ​​to create an image of a nurse, and she will probably attract a woman, because she has been trained on data that reflects “long -term prejudices that exist in society”, explains Seidl. “What are we doing inadvertently by making AI continue to perpetuate these things?”

There are also questions about academic integrity and the risk that users can rely too much on AI. Higher education must consider “where the end of legitimate academic aid ends and where dependence contrary to ethics begins,” explains Butcher.

Given the various ethical gray areas, higher education is challenged to establish childcare rails in these first days of the adoption of AI.

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