Hand-in-Hand: Ethics and Predictive Analytics

Almost every profession has a set of rules or ethical principles to ensure professionals provide quality service and engage in their craft responsibly. Most notably, these guidelines help professionals all of types—business, healthcare, and legal—interact in ways that don’t damage the reputable professions in which they practice or harm their clients. For example, many state bar associations have professional codes for attorneys outlining proper conduct when interacting with clients, other attorneys, and the court. Higher education, like any other profession, should also be governed by ethical standards.

Across the country, colleges and universities are turning increasingly to predictive analytics to help them recruit and educate students. Predictive analytics makes use of a college’s historical data to make predictions about (and influence) what might happen in the future. Predictive analytics has helped colleges attract the right number—and kind—of students needed to meet yearly revenue goals. And it has even helped one campus close the achievement gap for low-income and minority students.

Despite its promise, predictive analytics can have deleterious effects if not used ethically. For example, predictive analytics in the admissions process can justify not allocating resources to students who are predicted to not succeed at the college. The trouble is, few colleges have guidelines to ensure predictive analytics is used on campus responsibly.

Colleges must protect against intentionally or unintentionally using predictive analytics to thwart the success of students, especially students from underrepresented backgrounds. Today we are releasing Predictive Analytics in Higher Education: Five Guiding Practices for Ethical Use , a framework which lays out key considerations for college administrators, faculty, and staff. The guiding practices highlight the importance of intentional planning, robust support, good data, and effective interventions when using predictive analytics.

We hope this framework will be helpful for colleges at various stages of engagement with predictive analytics. Colleges just starting out should consider what predictive analytics will and won’t be used for. This can safeguard against these tools being used for nefarious reasons, like blocking college access for certain student populations. For colleges who are already using these tools, the framework highlights ways these colleges can ensure information on students and the institution is kept safe, is accurate, and helps them resolve problems on their campus.

Predictive tools have the potential to help solve the difficult puzzle of fluctuating enrollment, uncertain student engagement, and lagging student outcomes. While these are reasons to be excited, colleges need to insist that these tools are used responsibly. Predictive Analytics in Higher Education: Five Guiding Practices for Ethical Use will help colleges have the conversation about how to make the most out of predictive analytics, while focusing on the wellbeing of students. The business, healthcare, and legal professions have ethical practices that guide their craft. It’s time for higher education to join the club when it comes to carefully using predictive analytics.

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Authors:

Manuela Ekowo is a policy analyst with the Education Policy program at New America. She provides research and analysis on policies related to higher education including innovations in higher education delivery, the use of technology, open educational resources (OER), and ensuring equitable outcomes for underrepresented students.

Iris Palmer is a senior policy analyst with the Education Policy program at New America. She was previously a senior policy analyst at the National Governors Association.