Micro–Trust Repair in Human–AI Teams: Adapting Organizational Trust-Recovery Tactics to Digital Collaboration

Trust is central in digital Human–AI collaboration (Janhunen et al., 2024), but we know little about repairing trust after AI missteps. The trust-repair briefing (HRM International Digest, 2022) identifies mechanisms—effective information sharing, change-management expertise, and ethical behavior—that rebuild trust post-disruption. We propose a program of lab-in-the-field experiments in virtual teams where an AI teammate occasionally errs. We test micro-interventions inspired by trust-repair (transparent error disclosures, calibrated confidence, restitution options), paradox management (Luciano et al., 2024), and trauma-informed, person-centered care principles (Horan et al., 2022)—e.g., validating user emotion after errors, shared decision-making, and respectful language. We also leverage behavior-change insights for ethical tech design (Foster-Hanson & Venkatagiri, 2024): question default assumptions (“AI must always appear confident”), surface system complexity (explain uncertainty and trade-offs), and target social structures (norms for when humans can override AI). Novelty comes from synthesizing these human-focused literatures into actionable AI teammate behaviors and team protocols, then measuring downstream effects on knowledge sharing, resilience (Ding et al., 2024), and performance. This would move the Human–AI trust discourse from abstract principles to empirically validated repair playbooks.

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