The Fiduciary AI Standard is a governance idea, not a formal law you can look up in one official rulebook. The exact phrase is still emerging, so it is best understood as a principle: when an AI system acts on behalf of a person or organization, it should be designed to serve that principal’s interests with loyalty, care, and accountability.
The idea borrows from fiduciary duty, an old legal concept used for relationships where one party has special power or responsibility over another’s interests. In AI, researchers have argued that this lens can help designers ask practical questions: what context is the system operating in, who is it serving, what counts as that person’s best interest, how should conflicting interests be handled, and what would loyalty and care require in the system’s behavior?
This matters more as AI agents move from answering questions to taking action. A chatbot that drafts text creates one level of risk. An agent that buys software, changes data, reviews contracts, or makes recommendations inside a business process needs a higher bar. It should not just be capable. It should be constrained, monitored, and aligned with the interests it is supposed to serve.
For business leaders, the useful test is simple: whose interests does this AI system actually serve? Can it explain why it made a recommendation? Can people audit what it did? Who is accountable when it gets something wrong? Tools and practices around Explainable AI help because you cannot verify loyalty, care, or accountability in a system whose decisions are impossible to inspect.
If this concept has you thinking about how to make AI adoption safer and more accountable, Coursera’s AI Governance course offers a structured way to think through risk, oversight, and responsible AI decision-making.*