Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation
This addresses the problem of legal and public accountability in autonomous vehicle accidents, with implications for policy and safety regulations, but it is incremental as it builds on existing psychological and legal frameworks.
The study investigated how blame and causal responsibility are distributed between human and machine drivers in semi-autonomous vehicle accidents, finding that when both make errors, blame attributed to the machine is reduced, which could lead to under-regulation of AI safety.
When a semi-autonomous car crashes and harms someone, how are blame and causal responsibility distributed across the human and machine drivers? In this article, we consider cases in which a pedestrian was hit and killed by a car being operated under shared control of a primary and a secondary driver. We find that when only one driver makes an error, that driver receives the blame and is considered causally responsible for the harm, regardless of whether that driver is a machine or a human. However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced. This finding portends a public under-reaction to the malfunctioning AI components of semi-autonomous cars and therefore has a direct policy implication: a bottom-up regulatory scheme (which operates through tort law that is adjudicated through the jury system) could fail to properly regulate the safety of shared-control vehicles; instead, a top-down scheme (enacted through federal laws) may be called for.