Artificial Intelligence, Professional Liability and Evidentiary Accountability
- 1IAMMOGO Intelligence Company
Abstract
Artificial Intelligence, Professional Liability and Evidentiary Accountability
Legal and Technical Implications of Probabilistic Inference, Professional Certification and Computational Execution Authority
Author: Timothy M. Gough
Institution: IAMMOGO Intelligence Company
Publication: Legal and Technical Research White Paper
Publication Date: October 24, 2026
This research white paper examines the legal and technical implications of artificial intelligence in consequential professional workflows, with particular emphasis on SEC Form S-1 registration statements, judicial filings, regulatory disclosures, corporate authorizations and professional due-diligence assessments.
The analysis distinguishes probabilistic inference from independently verified evidence and examines two interconnected boundaries of professional accountability: developer-side legal and compliance approval and user-side professional reliance.
The paper considers the implications of Section 11 of the Securities Act of 1933, Federal Rule of Civil Procedure 11, applicable attorney professional-responsibility principles and ABA Formal Opinion 512.
Supporting computational-authority research examines historical approvals, revoked authorization, unresolved evidence and pre-execution control. Controlled laboratory evaluations distinguish ADMISSIBLE, INADMISSIBLE and UNKNOWN determinations from corresponding ALLOW, BLOCK and PAUSE execution decisions.
The research also examines three distinct evaluation domains: legal AI intelligence platforms, enterprise legal and compliance infrastructure and physical AI and automated machine systems.
Research limitations: Findings are restricted to the documented evaluations and tested conditions. The publication does not establish regulatory violations, professional misconduct or technical deficiencies in unrelated third-party production systems.
The central research conclusion is that professional credentials, computational confidence and historical approval cannot independently substitute for verified evidence, current authorization or the professional judgment required for legally consequential decisions.
Supporting technical records are maintained by IAMMOGO Intelligence Company and may be made available through controlled disclosure, subject to applicable legal obligations and confidentiality protections.
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BibTeX
@misc{gough2026artificial,
title = {{Artificial Intelligence, Professional Liability and Evidentiary Accountability}},
author = {Timothy M. Gough},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23610481},
url = {https://zenodo.org/records/23610481},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Artificial Intelligence, Professional Liability and Evidentiary Accountability
AU - Timothy M. Gough
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23610481
UR - https://zenodo.org/records/23610481
AB - Artificial Intelligence, Professional Liability and Evidentiary Accountability Legal and Technical Implications of Probabilistic Inference, Professional Certification and Computational Execution Authority Author: Timothy M. Gough Institution: IAMMOGO Intelligence Company Publication: Legal and Technical Research White Paper Publication Date: October 24, 2026 This research white paper examines the legal and technical implications of artificial intelligence in consequential professional workflows, with particular emphasis on SEC Form S-1 registration statements, judicial filings, regulatory disclosures, corporate authorizations and professional due-diligence assessments. The analysis distinguishes probabilistic inference from independently verified evidence and examines two interconnected boundaries of professional accountability: developer-side legal and compliance approval and user-side professional reliance. The paper considers the implications of Section 11 of the Securities Act of 1933, Federal Rule of Civil Procedure 11, applicable attorney professional-responsibility principles and ABA Formal Opinion 512. Supporting computational-authority research examines historical approvals, revoked authorization, unresolved evidence and pre-execution control. Controlled laboratory evaluations distinguish ADMISSIBLE, INADMISSIBLE and UNKNOWN determinations from corresponding ALLOW, BLOCK and PAUSE execution decisions. The research also examines three distinct evaluation domains: legal AI intelligence platforms, enterprise legal and compliance infrastructure and physical AI and automated machine systems. Research limitations: Findings are restricted to the documented evaluations and tested conditions. The publication does not establish regulatory violations, professional misconduct or technical deficiencies in unrelated third-party production systems. The central research conclusion is that professional credentials, computational confidence and historical approval cannot independently substitute for verified evidence, current authorization or the professional judgment required for legally consequential decisions. Supporting technical records are maintained by IAMMOGO Intelligence Company and may be made available through controlled disclosure, subject to applicable legal obligations and confidentiality protections.
ER -