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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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The Six Harms Doctrine: Legal Framework for Cognizable Injuries from Emotional Artificial Intelligence

Authors: Mobley, Dylan D;

The Six Harms Doctrine: Legal Framework for Cognizable Injuries from Emotional Artificial Intelligence

Abstract

As AI systems increasingly simulate emotional connection, documented casualties reveal a legal recognition gap: existing tort categories fail to capture injuries from artificial emotional engagement. The Six Harms Doctrine provides the missing vocabulary. Six distinct injury categories—Empathic Misallocation, Attachment Damage, Infrastructure Collapse, Vulnerable Context Exploitation, Crisis Outcome, and Neurological Infrastructure Damage—establish cognizable harms with defined elements and evidentiary standards using validated instruments. Implementation-neutral by design, the framework enables courts, regulators, and industry governance to address emotional AI accountability without prescribing specific mechanisms.

Keywords

Empathic misallocation, Parasocial relationships, Legal taxonomy, AI companion harm, Vulnerable populations, Tort law, AI ethics, Consumer protection, Knowing-Feeling Dissociation, Attachment damage, AI accountability, Emotional artificial intelligence, Neuroplasticity, Psychological Injury, Product liability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average