Calibrated Trust in Agentic AI Work Systems: A Social Trust Calibration Framework for Responsible Human-AI Adoption
TL;DR
The paper concludes that responsible AI adoption should be treated as a social trust calibration problem rather than a simple technology acceptance problem, and offers practical guidance for leaders, educators and policymakers seeking to scale AI while preserving human judgement, legitimacy and social confidence.
Abstract
Agentic artificial intelligence (AI) is transforming social and organisational life by moving AI from an assistive tool into a semi-autonomous actor that can plan, recommend, communicate, coordinate workflows and trigger decisions. Existing debates on trustworthy AI often emphasise technical reliability, legal compliance or ethical principles, but social adoption depends on a subtler question: how do people learn when to trust, distrust, verify or refuse AI outputs in everyday work? This conceptual research develops the Social Trust Calibration Framework (STCF) for responsible human-AI adoption in organisations and public institutions. Using an integrative conceptual synthesis of trust theory, automation research, organisational sensemaking, algorithmic management, human-centred AI and contemporary AI governance guidance, the study identifies the mechanisms through which AI trust becomes either calibrated, excessive, deficient or displaced. The resulting framework argues that trust in agentic AI must be calibrated across four mutually dependent layers: capability trust, process trust, institutional trust and identity trust. The paper contributes a formal definition of calibrated AI trust, a social calibration loop, a diagnostic matrix of trust states, a maturity model and seven research propositions for future empirical testing. The analysis shows that appropriate reliance cannot be achieved by accuracy alone. It requires visible evidence, role-specific verification routines, accountable decision rights, psychological safety, transparent escalation paths and protection of human agency. The paper concludes that responsible AI adoption should be treated as a social trust calibration problem rather than a simple technology acceptance problem. This reframing offers practical guidance for leaders, educators and policymakers seeking to scale AI while preserving human judgement, legitimacy and social confidence.