Communicating Threat

Abstract

This case study examines the deployment of Early Warning Systems (EWS) in Florida school districts, revealing how predictive analytics used to identify potential threats to school safety may reinforce systemic injustices. Originally intended to prevent school violence, these systems rely on data such as attendance, grades, and disciplinary records—factors often shaped by structural inequalities. The case investigates how EWS may conflate academic performance with mental health and violence risk, leading to the stigmatization and increased surveillance of already marginalized students. Despite limited causal evidence linking poor academic outcomes with violent behavior, EWS outputs are treated as diagnostic, enabling school personnel and law enforcement to intervene without clinical or contextual understanding. The study situates this practice within broader patterns of algorithmic bias, institutional mistrust, and racialized disciplinary policies, particularly impacting Black and Latinx students and those with disabilities. Ultimately, the case challenges the ethics of algorithmic governance in education, raising questions about equity, accountability, and the role of data-driven technologies in constructing students as threats.