Instrumental Trust
In The You On AI Field Guide
The absence of reciprocity is the defining feature and the core difficulty. In human relational trust, the other party can be held accountable, apologize, demonstrate reliability over time, and participate in the repair of broken trust. The AI system can do none of these things. When it produces output that proves wrong, there is no meaningful sense in which it can be held accountable for the error. When it produces output that proves useful, there is no meaningful sense in which it earned the user's subsequent confidence. The trust is therefore always one-directional — the user extends it or withdraws it without any reciprocal movement from the other side.
Brown's framework suggests that instrumental trust nonetheless requires behavioral supports analogous to BRAVING's relational components. Boundaries about acceptable AI use and its limits. Reliability assessments based on empirical track record rather than felt confidence. Accountability practices that assign human responsibility for AI-mediated outcomes. Vault-equivalent practices for data handling. Integrity norms about attribution and honest representation. Non-judgment environments in which users can report AI failures without stigma. Generous interpretation of colleagues' AI use patterns. The translation is not mechanical — each component requires rethinking — but the underlying framework holds.
The larger concern is that instrumental trust may be easier to extend than relational trust, precisely because it requires no reciprocity. The AI system does not disappoint in the specific way humans disappoint. It does not demand emotional investment in return. It does not judge the user's vulnerability. This asymmetric ease is part of what Brown called at the Aspen Ideas Festival the seductive alternative for tapping out of human vulnerability. The extension of instrumental trust to AI systems, combined with the withdrawal of relational trust from human colleagues, produces the hollowing Brown has warned about — not because the tools force the withdrawal but because they make the withdrawal less costly in the short term.
Origin
The concept is an extension of Brown's BRAVING framework to the specific case of human-AI interaction. It has been developed in organizational practice and emerging academic literature on human-AI collaboration rather than in Brown's direct writing, but the framework it extends and the questions it asks are consistent with her research trajectory.
Key Ideas
Absence of reciprocity. The AI system cannot participate in the mutual accountability relational trust requires.
One-directional extension. Trust flows from user to system without any movement from the other side.
BRAVING translation. Each relational-trust component requires rethinking rather than mechanical application to the AI case.
Seductive asymmetry. Instrumental trust is easier to extend than relational trust because it requires no vulnerability.
Hollowing risk. The extension of instrumental trust combined with withdrawal of relational trust produces the hollowing Brown has warned about.