Iris Palmer and Manuela Ekowo's report, Predictive Analytics in Higher Education, was cited by an EdSurge article on the potential use of big data in college advising:
Some analysts worry that, if not used correctly, these kinds of tools could reinforce existing biases in the education system, especially when demographic data—race, ethnicity, gender or socioeconomic status—is used to predict how successful a student will be. “We know that race, ethnicity and socioeconomic status tend to be correlated with at-risk status, and so students who come from similar groups might be disproportionately flagged or marked as one, at risk, or two, not having what it takes to pursue particular majors,” says Manuela Ekowo, policy analyst at New America.
In March, Ekowo co-authored a report on the ethical use of predictive analytics in higher education. “It is crucial,” reads the report, “that predictive models and algorithms are, at the very least, created to reduce rather than amplify bias.” The concern is: If students are deemed at-risk because of characteristics they can’t change, predictive analytics tools will reinforce existing problems, rather than helping to solve them. For instance: A minority student, predicted to perform poorly in her major because of her race, could be encouraged to switch to an easier major.
Should students know everything? It’s one of the hardest questions to grapple with, says Iris Palmer, a senior policy analyst who co-authored the New America report. She understands both sides of the debate: On one hand, there’s the concern that students have a right to their own data. But at the same time, “students are vulnerable to suggestion, and so when you just present things in a certain way, they can get easily discouraged.”