Dissecting Judicial Reasoning in U.S. Copyright Damage Awards
Pei-Chi Lo, Thomas Y. Lu

TL;DR
This paper presents a novel discourse-based LLM methodology to analyze judicial reasoning in U.S. copyright damage awards, revealing variability and improving interpretability of legal decision patterns.
Contribution
It introduces a hierarchical discourse parsing framework combining RST with LLMs, addressing a key gap in empirical legal analysis of judicial reasoning.
Findings
Discourse-augmented analysis outperforms traditional methods.
Uncovers variability in factor weighting across jurisdictions.
Provides insights into judicial reasoning patterns.
Abstract
Judicial reasoning in copyright damage awards poses a core challenge for computational legal analysis. Although federal courts follow the 1976 Copyright Act, their interpretations and factor weightings vary widely across jurisdictions. This inconsistency creates unpredictability for litigants and obscures the empirical basis of legal decisions. This research introduces a novel discourse-based Large Language Model (LLM) methodology that integrates Rhetorical Structure Theory (RST) with an agentic workflow to extract and quantify previously opaque reasoning patterns from judicial opinions. Our framework addresses a major gap in empirical legal scholarship by parsing opinions into hierarchical discourse structures and using a three-stage pipeline, i.e., Dataset Construction, Discourse Analysis, and Agentic Feature Extraction. This pipeline identifies reasoning components and extract…
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Taxonomy
TopicsComputational and Text Analysis Methods · Artificial Intelligence in Law · Legal Language and Interpretation
