When AI output tips to bad but nobody notices: Legal implications of AI’s mistakes

Neil F. Johnson

No z8md5_v1, LawArchive from Center for Open Science

Abstract: The adoption of generative AI across commercial and legal professions offers dramatic efficiency gains -- yet for law in particular, it introduces a perilous failure mode in which the AI fabricates fictitious case law, statutes, and judicial holdings that appear entirely authentic. Attorneys who unknowingly file such fabrications face professional sanctions, malpractice exposure, and reputational harm, while courts confront a novel threat to the integrity of the adversarial process. This failure mode is commonly dismissed as random `hallucination', but recent physics-based analysis of the Transformer's core mechanism reveals a deterministic component: the AI's internal state can cross a calculable threshold, causing its output to flip from reliable legal reasoning to authoritative-sounding fabrication. Here we present this science in a legal-industry setting, walking through a simulated brief-drafting scenario. Our analysis suggests that fabrication risk is not an anomalous glitch but a foreseeable consequence of the technology's design, with direct implications for the evolving duty of technological competence. We propose that legal professionals, courts, and regulators replace the outdated `black box' mental model with verification protocols based on how these systems actually fail.

Date: 2026-04-10
New Economics Papers: this item is included in nep-law
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/69c5771182fecec4041fe73f/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:lawarc:z8md5_v1

DOI: 10.31219/osf.io/z8md5_v1

Access Statistics for this paper

More papers in LawArchive from Center for Open Science
Bibliographic data for series maintained by OSF ().