Oct 2026· Zenodo (CERN European Organization for Nuclear Research)· 3 references
Artificial Intelligence in Law
Abstract
The phrase “AI democratizes legal analysis” circulates unsorted. This work sorts the three layers wrapped inside it — access to documents, the capacity to produce a deep diagnosis, and the examinability of the work — and then holds one position: that democratization is arriving at the layer of capacity but is not yet complete at the layer of trust. Capacity is now evenly spread. A sole advocate armed with a cheap language model can produce analysis as deep as what once demanded the layered working hours of a large firm. Yet evenly spread capacity is not equality. The large firm holds one mechanism of trust that a small firm cannot imitate: institutional accountability — the partner’s name staked, malpractice liability, layered review. The client trusts its analysis not by examining it, but by knowing who produced it. Here this work makes its turn. To rest trust on institutional accountability is to trust the origin, not the work — and that is precisely the old mechanism meant to be replaced. What completes democratization is not added capacity but a third pivot: the examinability of the work, that is, an output closed clause by clause to the primary source so that anyone can audit it without trusting the author’s lineage. This pivot is also what breaks the objection that AI merely retrieves documents and does not diagnose. The absence of any predecessor to this position was examined first through a search whose question and corpus boundaries were registered in advance (OSF 7w9b4; results report, Zenodo 10.5281/zenodo.23321290), with a null result. That absence establishes that this position has not been stated by others, not that it is true. Its truth is borne by the argument below, not by that absence.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
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