Employer’s Vicarious Liability for Damage Caused by an AI Worker: Comparative Law Perspective

The article examines whether the proposal to apply vicarious liability is the optimal method of regulating the employer’s liability towards third parties for damage caused by AI systems. The research concerns autonomous AI systems used by the employer to achieve the same goals as previously could be achieved only by the human workforce. In this article, the concept of the optimal solution refers to a liability model capable of addressing the challenges posed by the inherent characteristics of AI, such as autonomy, complexity, opacity, the ability to learn, and interaction with the environment, including humans. This model should ensure that injured parties have a genuine opportunity to receive compensation. Scholars have proposed the use of vicarious liability as, in their opinion, the optimal method to regulate employers’ liability vis-à-vis third parties for damage caused by autonomous AI systems. Experts predict that applying such systems on a large scale is only a matter of time. This article concentrates on the civil liability of an employer in situations where damage was sustained by a third party who is not in any legal relationship with the employer, and the damage was not caused by a defect of the AI system. The analysis covers the legal perspective of the EU and the US.1

Authors

Institutions

Publication Details

Journal
Utrecht Law Review
Published
2025-01-01
DOI
https://doi.org/10.36633/ulr.1063
Primary Topic
Corporate Law and Human Rights
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Employer’s Vicarious Liability for Damage Caused by an AI Worker: Comparative Law Perspective

Iwona Gredka-Ligarska
Utrecht Law Review
Corporate Law and Human Rights
article

Employer’s Vicarious Liability for Damage Caused by an AI Worker: Comparative Law Perspective

Iwona Gredka-Ligarska
article en

Abstract

The article examines whether the proposal to apply vicarious liability is the optimal method of regulating the employer’s liability towards third parties for damage caused by AI systems. The research concerns autonomous AI systems used by the employer to achieve the same goals as previously could be achieved only by the human workforce. In this article, the concept of the optimal solution refers to a liability model capable of addressing the challenges posed by the inherent characteristics of AI, such as autonomy, complexity, opacity, the ability to learn, and interaction with the environment, including humans. This model should ensure that injured parties have a genuine opportunity to receive compensation. Scholars have proposed the use of vicarious liability as, in their opinion, the optimal method to regulate employers’ liability vis-à-vis third parties for damage caused by autonomous AI systems. Experts predict that applying such systems on a large scale is only a matter of time. This article concentrates on the civil liability of an employer in situations where damage was sustained by a third party who is not in any legal relationship with the employer, and the damage was not caused by a defect of the AI system. The analysis covers the legal perspective of the EU and the US.1

Utrecht Law ReviewVol. 21(1)
University of Silesia in Katowice (PL)
Narodowym Centrum Nauki
Openalex Percentile: Top 73%
Corporate Law and Human Rights
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Employer’s Vicarious Liability for Damage Caused by an AI Worker: Comparative Law Perspective — Iwona Gredka-Ligarska · Utrecht Law Review (2025) | TGRS Research Map | TGRS