Technological Opacity, Predictability, and Self-Driving Cars

Harry Surden, Mary-Anne Williams
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引用次数: 51

Abstract

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 that 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 intention will be challenged in the context of non-human, autonomous moving entities such as self-driving cars.This article explains in detail how self-driving vehicles work and how their movements may be hard to predict. It then explores the role that law might play in fostering more predictable autonomous moving systems such as self-driving cars, robots, and drones.
技术的不透明性、可预测性和自动驾驶汽车
自动驾驶或“自动驾驶”汽车是在没有人类监督或输入的情况下自动驾驶的车辆。由于自动驾驶汽车有望带来安全方面的好处,它们可能会变得越来越普遍。值得注意的是,人们将第一次与计算机控制的机器共享一个物理环境,这些机器既可以指导自己的活动,又有相当大的活动范围。这代表着我们当前环境的一个明显变化。今天,人们要么与有自由活动范围但受人控制的机器(如汽车)共享物理空间,要么与由计算机控制但在活动范围上受到高度限制的机器(如电梯)共享物理空间。因此,今天机器的运行是可以大致预测的。不受限制的、由计算机控制的自动驾驶汽车的运动是一种全新的现象,可能会挑战我们现有法律结构中某些未明确的假设。问题是,与人类驾驶的汽车相比,自动驾驶汽车的运动可能更难以预测,因为普通人(比如行人)将共享他们的物理环境。今天,通过人类集体预测他人行动的能力,可能避免了大量原本可能发生的身体伤害。在预测他人的行为时,我们运用心理学家所说的“心理理论”。心理理论的认知机制,允许我们从自己的内部心理状态推断,以估计别人在想什么或可能做什么。这些认知系统使我们能够对周围人可能的行为做出即时的、无意识的判断,因此,在驾驶环境中保证自己的安全。然而,允许我们准确地模拟其他人的思想并解释他们的注意力和意图的交流信号的心智理论机制将在非人类的自主移动实体(如自动驾驶汽车)的背景下受到挑战。这篇文章详细解释了自动驾驶汽车是如何工作的,以及它们的运动是如何难以预测的。然后,它探讨了法律在培养更可预测的自主移动系统(如自动驾驶汽车、机器人和无人机)方面可能发挥的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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