Reducing Subjectivity and Bias in an Officer's Analysis of Suspicion in Drug Interdiction Stops

Police officers must daily determine whether they have justification to detain cars they have stopped for ordinary traffic investigations for further investigation. Yet these determinations involve the interpretation of very fact-specific case law that does not yield predictions for subsequent cases and are fraught with subjectivity if not actual bias. Artificially intelligent systems hold the potential to lessen the impact of implicit biases by assisting officers in making these decisions with greater consistency on the basis of factors relevant to suspicion. Using patented text recognition algorithms in order to identify content of interest, or relevant language, our prototype is capable of reading case law and police reports to identify factors relevant to suspicion. With this information, the likelihood a court will approve a search or detention can be assessed. Police reports identifying the bases for fruitful and unsuccessful searches will then permit the system to assess the odds that drugs are present. Deployment will further allow the collection of more detailed data about the basis of successful and unsuccessful stops, improving the system's predictive capacity.