The application of artificial intelligence in police interrogations: An analysis addressing the proposed effect AI has on racial and gender bias, cooperation, and false confessions

Abstract

Research presented in this study examines the potentiality of artificial intelligence as an interrogator within a police interrogation to promote a non-biased environment in an effort to mitigate the ongoing racial and gender divide in statistics regarding false confessions. Ideally, artificial intelligence supplementation may help promote the elicitation of non-coerced, voluntary confessions.This study suggests that the racial and gender bias influencing false confessions may be due to the two fold bias occurring within the interrogator-to-suspect dynamic, referenced in this study as “the Bias-Uncooperative Loop.” It argues that applying artificial intelligence within the interrogation room may minimize the two fold bias occurring in the dynamic. It suggests the potential for cooperation between the two parties can be conditioned by programmable similarity; whereby artificial intelligence can mimic the racial, ethnic and/or cultural similarities of the suspect in question. This is reflected in research in different arenas (not inclusive to interrogations) to have an effect on enhanced comfortability and cooperation with AI. This paper assumes similar results within interrogations.