Higher-education institutions' unregulated use of enrollment management algorithms may discriminate against low-income, female, and non-white applicants, critics say.
Research by the Brookings Institution cited findings from the nonprofit Educause that about 75% of higher-ed institutions were already using predictive data analytics to pick student applicants by 2015, a 15% gain from 2005. The study also said deploying the algorithms to boost enrollment and enhance fiscal planning without adequate safeguards could hinder diversity goals.
Brookings' Alex Engler warns that using predictive data analytics to measure students' ability to pay fees could create "subtle channels for algorithmic discrimination" against low-income and non-white students who already have reduced access to universities. He recommends institutions closely examine historical enrollment datasets fed into algorithms, as well as the context of students' fee-paying ability.
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