The Delegated Mandate: Generative AI in Arbitral Decision-Making and the Enforceability of Awards

Author

Sneha Patidar, Padmapriya Pradhan, Abhishek Rana


Abstract

  Generative artificial intelligence (AI) has moved from the margins of legal practice into the work of adjudicators. Existing scholarship on AI in arbitration has focused largely on efficiency gains in case management, document review and predictive analytics. This paper addresses a narrower and more consequential question: when an arbitrator relies on generative AI to analyse evidence, research the law or draft reasoning, has the arbitrator delegated a function that the parties entrusted to the arbitrator personally, and can such reliance affect the validity or enforceability of the resulting award? Adopting a doctrinal and comparative method, the paper examines the personal nature of the arbitral mandate, the grounds for setting aside and refusing enforcement under the New York Convention and the UNCITRAL Model Law, and emerging soft-law guidance. It argues that AI assistance is not unlawful in itself, but that undisclosed reliance which displaces the arbitrator’s own judgment exposes awards to challenge, and it proposes a disclosure-based framework in preference to a blanket prohibition.



Keywords

international arbitration; generative AI; arbitral mandate; delegation; New York Convention; enforcement; due process.



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References


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