Steven Van Uytsel
Steven Van Uytsel is Professor in the Faculty of Law at Kyushu University (Japan). He received his legal education at the University of Antwerp, including a semester length exchange at Uppsala University. He completed his LL.M. & LL.D. at Kyushu University and his Master of Arts (Japanese Studies) at the Mercator Hogeschool. Steven specializes in competition law, for which he has received several grants from the Japanese Society for the Promotion of Science. He was lead editor of Research Handbook on Asian Competition Law (Edward Elgar, 2020) and editor of Collective Actions: Enhancing Access to Justice and Reconciling Multilayer Interests (Cambridge, 2012). More recently, Steven has expanded his research to include artificial intelligence. For his research on artificial intelligence, he has obtained several grants such as JSPS Grant in Aid for Scientific Research (C) (April 2018-March 2021) for doing research on Artificial Intelligence, Price Setting Strategies and Antitrust Law: Towards a Regulatory Framework, Kyushu University’s Progress 100 – RINK Grant for researching Regulating Algorithms: Multi-Disciplinary Perspectives on New Technology & the Law (2018-2020) and Kyushu University’s Tsubasa Grant to research Misleading Algorithms: Interdisciplinary Perspectives on the Implications for Law (2018-2020). The latter mentioned grant has led, among others, to the following publications: Autonomous Vehicles Business, Technology and Law (Springer 2020, co-edited with D. V. Vargas); Testing Autonomous Vehicles On Public Roads: Facilitated by a Series of Alternative, Often Soft, Legal Instruments (Van Uytsel and Vasconcellos Vargas (eds.)) (Springer, 2020); Different Liability Regimes: One Liability Regime Preferable above the Other?, in Autonomous Vehicles: Business, Technology and Law (Van Uytsel and Vasconcellos Vargas (eds.)) (Springer, 2020); New Fixes for Old Traffic Problems: Connected Transport Systems and AIMES, in Autonomous Vehicles: Business, Technology and Law (Van Uytsel and Vasconcellos Vargas (eds.)) (Springer, 2020, co-authored with Majid Sarvi and Saeed Asadi); Adversarial Machine Learning: A Blow to the Transportation Sharing Economy. in Legal Tech and the New Sharing Economy (Corrales, Forgó, Kono, Teramoto, Vermeulen (eds.)) (Springer, 2020, co-authored with D.V. Vargas), pp. 179-208; Legislating Autonomous Vehicles against the Backdrop of Adversarial Machine Learning Findings, IEEE Xplore, https://ieeexplore.ieee.org/document/8965002 (2020), pp 1-10
Title
Autonomous Vehicles Confused: How Liability for Accidents Should Respond to Engineering Solutions for Adversarial Machine Learning
Abstract
Autonomous vehicles are said to bring safety to the roads. Machines are expected not to make the same driving mistakes as humans. Indeed, machines will not drive intoxicated or get too tired to drive. However, the application of adversarial machine learning to autonomous vehicles has shown that the reaction of these vehicles to altered traffic signs may be the cause of unpredictable reactions. Rather than stopping in front of a vandalized stop sign, the autonomous vehicle may speed. This may lead to accidents. Therefore, scholars have developed various liability and compensation schemes to deal with accidents by autonomous vehicles.
The following liability and compensation schemes have been suggested to deal with the civil liability of accidents of autonomous vehicles: operator liability, product liability, strict liability, no-fault compensation, and negligence. Each of these schemes are judged against victim and innovation friendliness. The former is being framed as easiness to obtain compensation, while the latter is understood as a burden on the industry.
Operator liability, strict liability and no-fault compensation are considered as victim friendly. Product liability and negligence put a burden on the victim to prove either a defect of the product or a fault of the manufacturer. Only by shifting the burden of proof to the manufacturer would these systems be made victim friendly. In terms of innovation, the situation is not obvious. Operator liability, product liability and negligence make it difficult for a manufacturer to anticipate the size of the financial burden in case of an accident. This would be different with strict liability and no-fault compensation.
Much of the discussion above is framed in relation to vehicles that are operating autonomously on their own. There is, however, more and more research on infrastructure enabled autonomy. In system, autonomous vehicles will be operating in connection with road side units, cloud services, and other traffic participants. As this will bring together products, services, and behavior, a mix of different liability regimes will make it difficult for the victim to obtain compensation. Therefore, a one-stop window may facilitate obtaining compensation. No-fault compensation could be ideal.