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38 Cardozo L. Rev. 121 (2016-2017)
Technological Opacity, Predictability, and Self-Driving Cars

handle is hein.journals/cdozo38 and id is 131 raw text is: TECHNOLOGICAL OPACITY, PREDICTABILITY, AND SELF-DRIVING CARS Harry Surdent & Mary-Anne Williamst Autonomous or self-driving cars are vehicles that drive themselves without human supervision or input. Because of safety benefits that they are expected to bring, autonomous vehicles are likely to become more common. Notably, for the first time, people will share a physical environment with computer-controlled machines that can both direct their own activities and that have considerable range of movement. This represents a distinct change from our current context. Today people share physical spaces either with machines that have free range of movement, but are controlled by people (e.g. automobiles) or with machines that are controlled by computers, but highly constrained in their range of movement (e.g. elevators). The movements of today's machines are thus broadly predictable. The unrestricted, computer-directed movement of autonomous vehicles is an entirely novel phenomenon that may challenge certain unarticulated assumptions in our existing legal structure. Problematically, the movements of autonomous vehicles may be less predictable to the ordinary people who will share their physical environment-such as pedestrians-than the comparable movements of human-driven vehicles. Today, a great deal of physical harm that might otherwise occur is likely avoided through humanity's collective ability to predict the movements of other people. In anticipating the behavior of others, we employ what psychologists call a theory of mind. Theory of mind cognitive mechanisms allow us to extrapolate from our own internal mental states in order to estimate what others are thinking or likely to do. These cognitive systems allow us to make instantaneous, unconscious judgments about the likely actions of people around us, and therefore, to keep ourselves safe in the driving context. However, the theory of mind mechanisms that allow us to accurately model the minds of other people and interpret their communicative signals of attention and t Professor of Law, University of Colorado Law School t Professor of Engineering and Robotics, University of Technology Sydney. We would like to thank Andrew Coan, Seema Shah, Carol Rose, Jane Bambauer, Larry Head, Eric Frew, David Levine, Bernard Chao, Blake Reid, Viva Moffat, Kristelia Garcia, Susan Nevelow-Mart, Colter Donahue, Helen Norton, Ashkan Soltani, Margot Kaminski, Sam Arbseman, and Ryan Calo for their helpful comments and suggestions. We would also like to thank the University of Colorado Law School, Stanford Law School CodeX Center, and the University of Technology Sydney for their support.

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