A growing body of scholarship is addressing the risks of opaque analyses as well as the fear of hidden biases and discrimination that may come along with automated decision-making. This work mirrors the growing public anxiety about the influence and lack of transparency of Big Data analysis practices. The current literature and related debate struggles with defining and revealing unfairness. As a possible strategy to respond to, and possibly preempt the fairness concerns, algorithmic examinations might seek out differences between inputs and outputs. This Article will focus on one form of such assessment – seeking out whether small changes in inputs produce large changes in the outputs. These are instances in which a “Small Change Makes a Big Difference” (or “SCMBD”). This simple form of analysis (which is both contemplated and already conducted) could prove to be an important innovation in building trusted and trustworthy AI systems and goes beyond merely examining the ex-post (under)representation of groups in the process’s outcome. To explore the nature and merit of SCMBD testing, this Article will proceed as follows. Beyond our introduction we will move, in Part II, to define SCMBD and confront challenges in attending to this concept’s scope. In Part III we will explain why SCMBD dynamics might be deemed unfair and therefore used to flag problematic algorithmic processes. In Part IV, we identify factors that would suggest that SCMBD is not problematic. In Part V, we conclude with policy recommendations to be applied in situations where we will find that SCMBD testing is appropriate. These include institutional and practical recommendations.