International Journal on Science and Technology

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Epistemic Calibration Networks: Bridging Human Illusions and Objective Signals in Multimodal AI

Author(s) Pinaki Bose
Country United States
Abstract This paper proposes the Epistemic Calibration Network (ECN), a novel sociotechnical framework designed to bridge the widening Epistemic Gap between human perception and AI output veracity. This gap is critically amplified by the proliferation of high-fidelity multimodal generative systems. The central problem is the divergence between the Human Illusion, wherein cognitive biases such as automation and normalization bias lead to critically miscalibrated human trust (over-reliance or under-reliance), and the AI’s Objective Signal, which is often poorly calibrated and susceptible to catastrophic failures like mismatched grounding. The ECN framework integrates three core, computationally modeled modules: (A) a Metacognitive Objective Signal Generator (M-OSG) utilizing cross-modal consistency for robust Uncertainty Quantification (UQ); (B) a Computational Human Bias Modeler (C-HBM) which predicts miscalibration risk based on derived cognitive profile ; and (C) a Dynamic Calibration Loop Interface (D-CLI) that employs adaptive, friction-based interventions . ECN provides an architectural blueprint for achieving genuine epistemic alignment, which is essential for fostering appropriate trust, ensuring robust decision-making, and facilitating ethical AI deployment in sensitive, high-stakes environments.
Keywords Epistemic Calibration, Multimodal AI, Uncertainty Quantification (UQ), Cognitive Bias, Human-AI Interaction, Metacognition, Trust Alignment, Sociotechnical Systems.
Field Engineering
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-10-22
DOI https://doi.org/10.71097/IJSAT.v16.i4.9554
Short DOI https://doi.org/hbb8f3
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