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JudgeAI Research
JudgeAI Research develops law as a computable procedure of normative choice. The program connects automated dispute resolution, AI lawmaking, and normative AI agents through one task: how a system generates, tests, and justifies legal choice from facts, participants, consequences, admissibility constraints, and evidence.
Publications
JudgeAI Research publications describe the development of one research program: from algorithmic dispute resolution to the formal conditions of autonomous normative choice.
Automated litigation
This paper sets the initial frame: court automation requires procedural inference that moves through facts, evidence, applicable law, and legal reasoning.
It describes JudgeAI as a system where the algorithmic layer matters because of predictability, transparency, and reduced arbitrariness.
Fully automated judiciary
The second paper shifts the question from automating individual court operations to a fully automated judicial procedure.
At the center is a combination of LLMs and logical algorithms: claim, defenses, evidence, and decision structure are analyzed as one process.
DeepTech adjudication
This article defines the boundary between LegalTech and DeepTech: an automated judge must reproduce legal reasoning through formalized norms, procedural steps, evidentiary analysis, and a verifiable inference structure.
Autonomous normative choice
The current work generalizes judicial and legislative modes into a single theory of autonomous normative choice. A normative decision is defined as the result of an internal logic where the admissibility of a rule depends on empirically observable consequences of its application.