Real-time triadic mediation: AI, epistemic symmetry, and preventive access to justice

Abstract

Millions navigate legal disputes without reliable knowledge of what the law establishes in their situation, and meet counterparties whose assumptions are mistaken in a different direction. This article develops real-time triadic mediation: an institutional model in which an AI system supplies simultaneous, source-grounded framework analysis to both parties in an emerging dispute, before positions harden. The model is preventive, bilateral, and framework-focused. Three premises support it, each defended and each bounded. The theoretical premise is that legal sources are constitutive, so that holdings retrieved under a doctrinally weighted scheme can support reliable framework guidance; the claim attaches to authority-weighted retrieval over a curated corpus rather than to statistical regularity across a mass of rulings, and does not survive the move to genuinely novel questions. The empirical premise is that divergent framework beliefs drive some share of escalation; legal needs research shows that this share has never been measured, and that the more prominent barrier is failure to characterise a problem as legal at all, so the premise is advanced as a hypothesis. The feasibility premise is that retrieval-augmented systems can reach the required reliability; preregistered evaluation of commercial legal research tools indicates they do not yet, which makes threshold conditions for deployment part of the model itself. Four worked examples illustrate the model, including one in which epistemic symmetry leaves the weaker party worse off. The article closes by arguing that non-deployment is itself a choice with distributive consequences, needing justification once the stated reliability conditions are met.