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

arXiv: https://arxiv.org/abs/1803.07170 · HTML: https://mlsanity.com/paper/1803.07170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models](https://mlsanity.com/paper/2604.20878/) (2026)
- [Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction](https://mlsanity.com/paper/2205.02987/) (2022)

## Abstract (arXiv)

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.