A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm
Identification
- Public canonical key
- doi:10.60082/2817-5069.3678
- Authors
- Pinchas Huberman
- Reported year
- 2021
- Reported language
- en
- Source type
- article
- First detected
- 2026-11-09T19:04:47.876685+00:00
- Last metadata observation
- 2026-11-09T19:04:47.876685+00:00
- Last HTTP verification
- Not checked
Institutions reported by the source
University of Toronto · Yale University
Reported affiliation countries
CA · US
Documentary abstract — original language
The possibility of autonomous-machine-caused harm generates doctrinal and theoretical challenges for assigning tort liability. With emergent capabilities, autonomous machines disrupt the structure of interpersonal rights and duties in tort law, framed by conditions of foreseeability and proximate causation. Where algorithmic processes are unintelligible, self-modifying, and unpredictable, the concern goes, algorithmic harms will be untraceable to tortious human agency. As a result, their costs will simply lie where they fall—on faultless victims. This outcome would be unfair and objectionable: A failure of tort’s mechanisms of corrective justice means faultless victims would disproportionately bear the accident costs of autonomous machines. This article suggests that the doctrinal form of vicarious liability is a promising strategy to ground tort liability for autonomous-machine-caused harm. Human or corporate deployers should be held liable for tortious harm caused by autonomous machines in the course of deployment. In this account, autonomous machines constitute a novel legal category as pure legal agents without legal personhood. In reconceiving vicarious liability—and the legal classification of autonomous machines—the article seeks to promote commonsensical liability outcomes for autonomous-machine-caused harm, consistent with tort’s doctrinal and theoretical structure of corrective justice.
Identifiers
- OPENALEX
- W3207087469
- DOI
- 10.60082/2817-5069.3678
Documentary context
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- Scholarship
- Source-reported types
- article
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Sources
OpenAlex · Record 1 · Record 2
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- openalex:w3124739569 · CITES
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Cite this reference
Pinchas Huberman. A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm. 2021. https://doi.org/10.60082/2817-5069.3678
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APA, Chicago, ISO 690: bibliographic formats using available fields. OSCOLA / Bluebook: structured data only; full legal citation compliance is not claimed without verification of required elements.
- APA
- Pinchas Huberman. (2021). A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm. https://doi.org/10.60082/2817-5069.3678
- Chicago
- Pinchas Huberman. A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm. 2021. https://doi.org/10.60082/2817-5069.3678
- ISO 690
- Pinchas Huberman. A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm. 2021. https://doi.org/10.60082/2817-5069.3678
UDTR Global Rights of Nature Legal Library. A Theory of Vicarious Liability for Autonomous-Machine-Caused Harm. Documentary record: https://www.declarationuniverselledesdroitsdelarbre.org/global-rights-of-nature-radar/reference/r-7eab2b2dfd2d9807f3e963f59e7a7e11/ · Last HTTP verification: not performed
Field provenance and record version
Version 8960be3dbbe46422 · udtr-reference-1.1.2
- Title
- OpenAlex · source · 2026-11-09T19:04:47.876685+00:00
- Authors
- OpenAlex · source · 2026-11-09T19:04:47.876685+00:00
- Year
- OpenAlex · source · 2026-11-09T19:04:47.876685+00:00
- Identifiers
- OpenAlex · source · 2026-11-09T19:04:47.876685+00:00
- Source URL
- OpenAlex · source · 2026-11-09T19:04:47.876685+00:00
History tracks record metadata and does not certify the state of the law.
e184a4fc4d907145 · 2026-11-09T19:04:47.876685+00:00
6951fc98137fe02c · 2026-11-09T19:04:47.876685+00:00
c6ad9a5025da7a9e · 2026-11-09T19:04:47.876685+00:00
8960be3dbbe46422 · 2026-11-09T19:04:47.876685+00:00
Research dossiers and documentary relations
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Discovery source · https://doi.org/10.60082/2817-5069.3678
Discovery source · https://openalex.org/W3207087469
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