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51 Geo. J. Int'l L. 195 (2019-2020)
AI in the Courtroom: A Comparative Analysis of Machine Evidence in Criminal Trials

handle is hein.journals/geojintl51 and id is 200 raw text is: ARTICLES Al IN THE COURTROOM: A COMPARATIVE ANALYSIS OF MACHINE EVIDENCE IN CRIMINAL TRIALS SABINE GLESS* ABSTRACT 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 jus- tice systems across the world due to the fact that they have traditionally been tai- lored 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 gener- ated 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 crimi- nal 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 * Sabine Gless is a Professor of Criminal Law and Procedure at the University of Basel School of Law (Switzerland) where she holds a Chair in Criminal Law and Criminal Procedure. She may be reached at Sabine.Gless(unibas.ch. This Article is the result of her participation in NYU's Hauser Global Scholarship Program during the Spring of 2019. Special thanks goes to all NYU School of Law faculty and staff for their support and the Swiss National Research Foundation for their ongoing support and funding, in particular the NFP75 Big Data grant. Furthermore, the author wishes to thank Griinne de Bnrca, Sara Beale, Eric Hilgendorf, Suzanne Kim, Erin Murphy, Richard Myers, Catherine Sharkey, Kathrin Strandburg, Thomas Weigend, Sarah Wood, and the participants of the Emile No81le & Hauser Workshop in March 2019 as well as the participants of the Data, Technology and Criminal Law Workshop at Duke Law School in April 2019 for their comments. © 2020, Sabine Gless. 195

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