Tort Before Retrieval Structural Fiscalistics and the Pre-Analytical Conditions of Legal AI

This working paper argues that legal reasoning in rule-dense systems does not begin with retrieval, but with a prior institutional act of intake. Contemporary Legal AI often assumes that the legal object already exists in a stable form that can be searched, matched, summarized, and reasoned over. This paper challenges that assumption. Before authorities can be retrieved, compared, or applied, a raw social event must first be admitted, reduced, classified, and routed into a legally intelligible object. The paper develops Tort Before Retrieval (TBR) as a theory of pre-doctrinal legal intake. TBR explains how tort law often functions as an early grammar through which diffuse factual situations are compressed into potentially actionable legal forms. It then introduces Structural Fiscalistics as a theory of the institutional conditions that make legal intake supportable: evidentiary admissibility, classificatory boundaries, routing containers, and resource-bearing capacity. Together, TBR and Structural Fiscalistics show that legal perception is institutionally produced before legal reasoning becomes searchable. The paper applies this framework to Legal AI by identifying the limits of archive-bound intelligence. Retrieval systems are strongest when the legal object has already been stabilized within a searchable archive. They are weakest at the earlier stage where admissible facts, responsibility pathways, and legal containers must first be formed. The central problem is therefore not only that AI may retrieve the wrong authority, but that retrieval begins after the decisive act of legal object formation has already occurred. In generalized form, the paper links this argument to the viability relation FGC = X × Y, where technical-preparatory capacity is insufficient without institutionally valid closure. It also extends the framework toward the Cabinet-Drawer Model as a way of describing institutional lodging, routing, and containment. The paper’s core claim is that law begins not by searching what it already knows, but by deciding under structured institutional conditions what counts as a legal object capable of being known.

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Publication Details

Journal
TSpace
Published
2026-04-25
DOI
https://doi.org/10.5281/zenodo.19763212
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Tort Before Retrieval Structural Fiscalistics and the Pre-Analytical Conditions of Legal AI

Yongzhi Huang
TSpace
Ethics and Social Impacts of AI
article

Tort Before Retrieval Structural Fiscalistics and the Pre-Analytical Conditions of Legal AI

Yongzhi Huang
article en

Abstract

This working paper argues that legal reasoning in rule-dense systems does not begin with retrieval, but with a prior institutional act of intake. Contemporary Legal AI often assumes that the legal object already exists in a stable form that can be searched, matched, summarized, and reasoned over. This paper challenges that assumption. Before authorities can be retrieved, compared, or applied, a raw social event must first be admitted, reduced, classified, and routed into a legally intelligible object. The paper develops Tort Before Retrieval (TBR) as a theory of pre-doctrinal legal intake. TBR explains how tort law often functions as an early grammar through which diffuse factual situations are compressed into potentially actionable legal forms. It then introduces Structural Fiscalistics as a theory of the institutional conditions that make legal intake supportable: evidentiary admissibility, classificatory boundaries, routing containers, and resource-bearing capacity. Together, TBR and Structural Fiscalistics show that legal perception is institutionally produced before legal reasoning becomes searchable. The paper applies this framework to Legal AI by identifying the limits of archive-bound intelligence. Retrieval systems are strongest when the legal object has already been stabilized within a searchable archive. They are weakest at the earlier stage where admissible facts, responsibility pathways, and legal containers must first be formed. The central problem is therefore not only that AI may retrieve the wrong authority, but that retrieval begins after the decisive act of legal object formation has already occurred. In generalized form, the paper links this argument to the viability relation FGC = X × Y, where technical-preparatory capacity is insufficient without institutionally valid closure. It also extends the framework toward the Cabinet-Drawer Model as a way of describing institutional lodging, routing, and containment. The paper’s core claim is that law begins not by searching what it already knows, but by deciding under structured institutional conditions what counts as a legal object capable of being known.

TSpace
University of Toronto (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 5%
Ethics and Social Impacts of AI
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