Trust calibration in human-AI collaborative decision-making: a cross-domain investigation of climate intelligence and organisational governance systems

Abstract The proliferation of artificial intelligence (AI) in complex decision environments has intensified scholarly and practical interest in how humans calibrate trust when collaborating with intelligent systems. This paper presents a cross-domain empirical investigation of human-AI collaborative decision-making across climate intelligence and organisational governance contexts. Drawing on a mixed-methods synthesis of 68 primary studies, original survey data from 200 industry professionals, and 30 semi-structured interviews, we examine trust calibration dynamics, performance outcomes, and implementation barriers. Our findings reveal a systematic trust calibration paradox: while 75% of organisations reported efficiency gains from AI integration, only 45% perceived their systems as transparent and explainable, and 38% identified persistent algorithmic bias. Effect sizes varied substantially across applications, reaching g = 1.65 for extreme-weather detection, with considerable between-study heterogeneity. Critically, we find that trust exhibited a non-linear association with AI autonomy, with higher observed trust at intermediate than at maximal levels of delegation; turning regions varied by decision context and should not be interpreted as universal autonomy thresholds. These findings advance human-intelligent systems integration theory by demonstrating that effective collaboration depends less on algorithmic performance alone and more on calibrated trust, institutional capacity, and governance frameworks that sustain human agency within hybrid decision architectures.