Computationally Assessing Suspicion

Law enforcement officers performing drug interdiction on interstate highways have to decide nearly every day whether there is reasonable suspicion to detain motorists until a trained dog can sniff for the presence of drugs. The officers’ assessments are often wrong, however, and lead to unnecessary detentions of innocent persons and the suppression of drugs found on guilty ones. We propose a computational method of evaluating suspicion in these encounters and offer experimental results from early efforts demonstrating its feasibility. With the assistance of large language and predictive machine learning models, it appears that judges, advocates, and even police officers could more effectively access the thousands of judicial opinions that have considered this issue—the legality of continued detention.

In developing a predictive model, implicit biases in judicial decision-making may also be unearthed, potentially providing police departments with the tools to modify policies—and courts the rationale to rethink precedent—to make the reasonable suspicion standard more racially neutral in application.