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Independent public-source case study
Mata v. Avianca, Inc.
AI-generated judicial authorities, failed citation verification and the professional gatekeeping problem.
Case type: Independent public-source demonstrationClient status: Not a client engagementEvidence cut-off: 5 August 2026Public status: Human-approved public portfolio PDF
Full public PDF available
Inspect the complete human-approved public portfolio.
The public PDF is the human-approved Public Portfolio Edition for this demonstration. It gives prospective clients, researchers and institutions a substantive view of the evidence problem, verification method, findings and reliance boundaries without exposing controlled internal research materials.
Judicial authorities and legal propositions cannot be treated as reliable merely because they appear in a professionally formatted filing. The underlying authorities must exist, be correctly identified and support the proposition for which they are cited.
The verification failure
The public record documents AI-generated authorities and a breakdown in human citation verification. The case is useful because it separates tool use from the professional duty to verify the source before reliance.
Why it matters beyond law
The same control problem exists in academic and institutional research: fluent text, repeated citations and apparent authority do not substitute for opening the source, checking provenance and testing proposition fit.
Responsible boundary
This is evidence-assurance research, not legal advice, disciplinary opinion or expert evidence. The public case demonstrates source-control methods and reliance discipline.
Academic relevance
A direct research-integrity and AI-verification sample.
For universities, research offices, faculty and postgraduate research teams, this case is a concrete demonstration of why citation existence, source identity, quoted meaning and proposition fit should be checked before a finding is relied upon or published.
citation and authority verification
source provenance and proposition-fit testing
AI-output verification under human responsibility
documentary reconstruction of what failed and when
clear separation between established facts, inference and unresolved material
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