THE CAPACITY TO PROVE: Governance and Trust in the Age of AI
THE CAPACITY TO PROVE:
Governance and Trust in the Age of AI
Written by:
Dr. Severo C. Madrona Jr.
Department of Commercial Law, RVR-College of Business DLSU-Manila
Introduction
Artificial intelligence (AI) allows firms to decide, communicate, contract, market, and extend credit at unprecedented speed while making responsibility and authenticity harder to establish. Synthetic content can resemble evidence; automated decisions can obscure accountability; environmental claims can outrun substantiation; platforms can restrict access to business data; code can execute obligations before disputes emerge; and frictionless credit can conceal consumer risk. These problems appear legally distinct but present a common governance challenge: can the firm prove that a consequential digital action was accountable, authentic substantiated, and contestable?
This Policy Brief calls a digital action consequential when it can materially affect rights, money, contractual obligations, customer access, regulatory compliance, or reputation. A material AI application is an AI use that can make or materially influence such an action. Trust-by design means integrating accountability, authenticity, substantiation, and contestability into the workflow before deployment or publication, rather than adding controls only after a failure. The result sought is verifiable trust: the firm’s capacity to demonstrate how a consequential digital action occurred, who was responsible, what evidence supported it, and how an error can be challenged or corrected.
The brief proceeds through doctrinal analysis of Philippine electronic commerce and evidence law, a comparison with European Union, United States, and United Nations Commission on International Trade Law (UNCITRAL) instruments, and reference to three empirical studies on greenwashing, data portability, and Buy Now, Pay Later (BNPL). The trust-by design model is the author’s normative synthesis of these materials; it is proposed as a managerial framework and has not itself been empirically tested
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