  Trusted AI literature to date has focused on the trust needs of users who
knowingly interact with discrete AIs. Conspicuously absent from the literature
is a rigorous treatment of public trust in AI. We argue that public distrust of
AI originates from the under-development of a regulatory ecosystem that would
guarantee the trustworthiness of the AIs that pervade society. Drawing from
structuration theory and literature on institutional trust, we offer a model of
public trust in AI that differs starkly from models driving Trusted AI efforts.
This model provides a theoretical scaffolding for Trusted AI research which
underscores the need to develop nothing less than a comprehensive and visibly
functioning regulatory ecosystem. We elaborate the pivotal role of externally
auditable AI documentation within this model and the work to be done to ensure
it is effective, and outline a number of actions that would promote public
trust in AI. We discuss how existing efforts to develop AI documentation within
organizations -- both to inform potential adopters of AI components and support
the deliberations of risk and ethics review boards -- is necessary but
insufficient assurance of the trustworthiness of AI. We argue that being
accountable to the public in ways that earn their trust, through elaborating
rules for AI and developing resources for enforcing these rules, is what will
ultimately make AI trustworthy enough to be woven into the fabric of our
society.
