Differential item functioning (DIF) refers to the situation where responses to a given question on an exam (or survey or similar) differ between several groups. For several decades now, social scientists and education researchers have employed a standard battery of statistical tools to detect DIF from sample data, but essentially all of these standard tools rely on theoretical asymptotic results and presuppose sample sizes that are rarely achieved by real data sets. In this talk, we'll discuss how ideas dating back to Diaconis and Sturmfels, in which techniques from computational algebra are brought to bear in statistics, provide an alternative method to detect DIF which avoids asymptotics and is more robust with smaller sample sizes. This is joint work with Luis David Garcia-Puente, Minho Kim, and Flavia Sancier-Barbosa.