This paper analyzes the growing incorporation of predictive algorithmic tools across appellate judicial frameworks. Synthesizing doctrinal precedents from 240 jurisdictions alongside empirical caseload metrics, we establish an evidence-based risk threshold for judicial automation.
AIT: AIT-2026-00042
JIT: JIT-RESPO-01
DOI: 10.5555/respo.2026.00042
IIT: IIT-NLSIU-01 Empirical Legal Studies
A Doctrinal and Empirical Synthesis of Algorithmic Governance in Judicial Decision-Making
Authors & Affiliations (1 Contributor)
Dr. Aarav Ramanathan 1st Author
Professor of Jurisprudence & Empirical Law • National Law School of India University (NLSIU)
Journal: RESPO Journal of Empirical Legal Studies •
ISSN: 2990-1234 •
Vol. 4, Issue 2 (2026)
Received: Oct 4, 2026 | Published: 2026-10-04 07:35:01
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Abstract
This paper analyzes the growing incorporation of predictive algorithmic tools across appellate judicial frameworks. Synthesizing doctrinal precedents from 240 jurisdictions alongside empirical caseload metrics, we establish an evidence-based risk threshold for judicial automation.
Keywords:
algorithmic governance, doctrinal law, judicial analytics, predictive justice
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@article{admin2026_27,
title = {A Doctrinal and Empirical Synthesis of Algorithmic Governance in Judicial Decision-Making},
author = {admin},
journal = {RESPO Journal of Empirical Legal Studies},
volume = {1},
number = {1},
pages = {1--10},
year = {2026},
doi = {10.5555/respo.2026.00042},
note = {AIT: AIT-2026-00042},
url = {https://respo.iledu.in/articles/a-doctrinal-and-empirical-synthesis-of-algorithmic-governance-in-judicial-decision-making/}
}