Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation

TL;DR

This study explores how blame and responsibility are assigned in accidents involving semi-autonomous vehicles, revealing biases towards human blame and highlighting policy implications for regulation of AI-driven cars.

Contribution

It provides empirical insights into blame attribution in shared-control vehicle accidents, emphasizing potential shortcomings of current legal frameworks.

Findings

Blame is assigned primarily to the driver who makes an error.

When both drivers err, blame shifts away from the machine.

Public under-reaction to AI malfunctions may occur under current legal systems.

Abstract

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.