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The Death and Life of an Admissions Algorithm


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University of Texas at Austin's main building

UT Austin said the ML-based GRADE is being phased out because it is too difficult to maintain.

Credit: Wikimedia

In 2013, the University of Texas at Austin's computer science department began using a machine-learning system called GRADE to help make decisions about who gets into its Ph.D. program — and who doesn't. This year, the department abandoned it.

Before the announcement, which the department released in the form of a tweet reply, few had even heard of the program. Now, its critics — concerned about diversity, equity, and fairness in admissions — say it should never have been used in the first place.

GRADE was created by a UT faculty member and UT graduate student in computer science, originally to help the graduate admissions committee in the department save time. The creators trained GRADE on a database of past admissions decisions.

Manish Raghavan, a computer science Ph.D. candidate at Cornell University who has researched and written about bias in algorithms, said there appeared to be no effort to audit the impacts of GRADE, such as how scores differ across demographic groups.

"Every process is going to make some mistakes," he said. "The question is, where are those mistakes likely to be made and who is likely to suffer as a result of them?"

From Inside Higher Ed
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