Human-In-The-Loop Vs. Fully Autonomous Enterprise Ai Agents: A Trust Calibration Framework For Governance, Control, And Decision-Making
DOI:
https://doi.org/10.63665/IJAICE.0203.15Keywords:
Enterprise AI Agent Trust Calibration Framework (EAATCF), Enterprise AI Agents, Trust Calibration, Human Oversight, Autonomy, Reliability, Decision Risk, Explainability, Reversibility, Accountability, Dominant Autonomy, AI GovernanceAbstract
This study develops an Enterprise AI Agent Trust Calibration Framework (EAATCF) that can be used to establish proper human control and autonomy of enterprise AI agents. The framework incorporates reliability, decision risk, explainability, reversibility and accountability, and is tested on synthetic data, machine-learning classification, dynamic recalibration, and robustness. Findings identify reliability and risk as determinants of dominant autonomy. While the intermediate levels of autonomy occur more often compared to the extremes of control. Conditional robustness to weights, distributions and thresholds are shown by sensitivity analyses, though there is a restriction to extreme-level prediction in terms of imbalance of minority classes. EAATCF brings in a computationally consistent, context sensitive framework of matching trust, governance, control and responsible enterprise decision-making.
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