As applications climb and admissions staffing stays flat, more colleges are handing the search for "right-fit" applicants to predictive algorithms built and sold by outside consulting firms. This essay argues that the efficiency comes at a cost — a quiet surveillance system built on the very students being recruited.
What happens when colleges outsource the job of identifying "right-fit" applicants to predictive algorithms built and sold by third-party consulting firms?
These "Algorithms as a Service" systems normalize a surveillance regime that reduces a prospective student to a score — trading accuracy and insight for perceived efficiency, and reifying the biases of both their authors and their data.
The harm isn't evenly distributed. A system built on biased data and biased authorship lands hardest on the students already struggling most — underrepresented and lower-income applicants.
This algorithm serves to fill a perceived need for maximizing efficiency within a system that is fraught with problems to begin with, and in doing so, exacerbates and amplifies biases already present within higher education. While this has the potential to harm all prospective students, the reality is that it is most likely to have the greatest negative impact on those who already experience significant struggles — particularly underrepresented populations and those of lower socioeconomic status.
Full paper available on request.