Truth And Technology: Deepfakes in Law Enforcement Interrogations

Abstract

The increasing deployment of generative artificial intelligence (AI) in law enforcement raises pressing theoretical and normative questions, particularlyregardingits potental to infecta critical aspect of a police investigation, the custodial interrogation. A central issue is whether, and to what extent, police may lawfully employ-generative Al tools to induce confessions from suspects during custodial questioning. Historically, courtshave permitted law enforcement officers to use certain deceptive tactics during interrogations withoutrunningafoulofconstitutionalprotections. Permissible forms ofdeception have included the presentation offalsiied forensic evidence, fabricated witness statements, and pretend polygraph results. These methods, though controversial, have long been accepted as part of the strategic repertoire oflaw enforcement on the premise that they do not overbear the suspect's will to a constitutionallyimpermissible degree. However, the advent of generative AI introduces new dimensions of manipulation that may significandy intensify the inherent compulsion of a custodial interrogation. AI-generated "deepfakes'-synthetic audio, video, orimages thatappear convincingly authentic-can be used to fabricate incriminating evidence with unprecedented realism. Imagine a scenario in which police confronta suspect with a deepfaked video depicting a purported eyewitness who falsely claims to have observed the suspect commiting the crime, or a video of an "accomplice" confessing and implicating the suspect. Such fabrications, made possible by AI, could profoundly distort a suspect's perception of the evidence against them, potentially exceeding the bounds of due process and exacerbating the potential for false confessions. Beyond the presentation offalse evidence, generative Almayalso enable real-time interrogation enhancements. Advanced models may-soon analyze inputs, such as a suspect's facial expressions, vocal tone, posture, and demographic characteristics, and generate immediate feedback or strategy suggestions for interrogators. These AI-driven analytics could guide law enforcement toward more psychologically effective, and possibly more coercive, techniques tailored to the individual suspect. The potential use of such data-driven, adaptive interrogation strategies raises serious concerns under established voluntarinessjurisprudence. To address the unprecedented risks posed by-generative Al in the interrogation context, courts should adopta clear and administrable per se rule: that the use offabricated evidence generated by Al to elicit a confession renders that confession involuntary. As generative Al enables increasinglypersuasive and deceptive forms ofevidence fabrication, a categoricalprohibition on its use in custodial interrogations is both doctrinally sound and normatively imperative. This approach would preserve the integrityofthe criminaljustice system and ensure that constitutional protections remain robustin the face ofrapidly advancing digital manipulation.