Redesigning Disclosure Norms for AI-Mediated Commercial Negotiations

Abstract

In traditional commercial bargaining, parties are often trapped in a "bargainer’s dilemma": the strategic withholding of private information to protect a bargaining position frequently results in inefficient contracts. This paper proposes a transformative solution through an information intermediary system—an AI capable of ingesting private inputs from both parties to formulate optimal contract terms without necessarily revealing the raw data to the counterparty. The authors argue for a shift from binary disclosure regimes to a multi-layered revelation framework comprising AI-Exclusive Revelation - Data used solely for algorithmic contract design; Contingent Ex Post Disclosure: Information revealed only upon execution to prevent "information theft" during failed negotiations; and Traditional Ex Ante Disclosure: Retaining existing discovery mechanisms where necessary. By preempting strategic withholding, this algorithmic architecture maximizes joint surplus. The paper posits that future legal obligations should evolve to incentivize the adoption of such algorithmic intermediaries over direct ex ante discovery.