Volume 51
Issue 2
Winter '20

AI in the Courtroom: A Comparative Analysis of Machine Evidence in Criminal Trials

Written By: Sabine Gless


As artificial intelligence (AI) has become more commonplace, the monitoring of human behavior by machines and software bots has created so-called machine evidence. This new type of evidence poses procedural challenges in criminal justice systems across the world due to the fact that they have traditionally been tailored for human testimony. This article’s focus is on information proffered as evidence in criminal trials which has been generated by AI-driven systems that observe and evaluate the behavior of human users to predict future behavior in an attempt to enhance safety. A poignant example of this type of evidence stemming from data generated by a consumer product is automated driving, where driving assistants as safety features, observe and evaluate a driver’s ability to retake control of a vehicle where necessary. In Europe, for instance, new intelligent devices, including drowsiness detection and distraction warning systems, will become mandatory in new cars beginning in 2022. In the event that human-machine interactions cause harm (e.g., an accident involving an automated vehicle), there is likely to be a plethora of machine evidence, or data generated by AI-driven systems, potentially available for use in a criminal trial. It is not yet clear if and how this the data can be used as evidence in criminal fact-finding, and adversarial and inquisitorial systems approach this issue very differently. Adversarial proceedings have the advantage of partisan vetting, which gives both sides the opportunity to challenge consumer products offered as witnesses. By contrast, inquisitorial systems have specific mechanisms in place to introduce expert evidence recorded outside the courtroom, including to establish facts, which will be necessary to thoroughly test AI. Using the German and the U.S. federal systems as examples, this Article highlights the challenges posed by machine evidence in criminal proceedings. The primary area of comparison is the maintenance of trust in fact-finding as the law evolves to accommodate the use of machine evidence. This comparative perspective illustrates the enigma of AI in the courtroom and foreshadows what will become inevitable problems in the not-too-distant future. The Article concludes that, at present, criminal justice systems are not sufficiently equipped to deal with the novel and varied types of information generated by embedded AI in consumer products. It is suggested that we merge the adversarial system’s tools for bipartisan vetting of evidence with the inquisitorial system’s inclusion of out-of-court statements under specific conditions to establish adequate means of testing machine evidence.

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