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U of Texas at Austin has stopped using a machine-learning system to evaluate applicants for its Ph.D. in computer science. Critics say the system exacerbates existing inequality in the field, general assignment reporter, Lilah Burke, for Inside Higher Ed tells.
The present Main Building (University of Texas at Austin) designed by Paul Philippe Cret.
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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.
“Humans code these systems. Humans are encoding their own biases into these algorithms,” said Yasmeen Musthafa, a Ph.D. student in plasma physics at the University of California, Irvine, who rang alarm bells about the system on Twitter. “What would UT Austin CS department have looked like without GRADE? We’ll never know.”
GRADE (which stands for GRaduate ADmissions Evaluator) 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...
GRADE’s creators have said that the system is only programmed to replicate what the admissions committee was doing prior to 2013, not to make better decisions than humans could. The system isn’t programmed to use race or gender to make its predictions, they’ve said. In fact, when given those features as options to help make its predictions, it chooses to give them zero weight. GRADE’s creators have said this is evidence that the committee’s decisions are gender and race neutral.
Detractors have countered this, arguing that race and gender can be encoded into other features of the application that the system uses. Women’s colleges and historically Black universities may be undervalued by the algorithm, they’ve said. Letters of recommendation are known to reflect gender bias, as recommenders are more likely to describe female students as “caring” rather than “assertive” or “trailblazing.”
Source: Inside Higher Ed
