Trustworthiness in neural networks is crucial for their deployment in critical applications, where reliability, confidence, and uncertainty play a pivotal role in decision-making. Traditional performance metrics such as accuracy and precision fail to capture these aspects, particularly in cases where models exhibit overconfidence. To address these limitations, this paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Calibration Error (ECE). This method provides a comprehensive measure of trust, distrust, and uncertainty by clustering predicted probabilities and fusing opinions using appropriate fusion operators. We demonstrate the effectiveness of this approach through experiments on the MNIST and CIFAR-10 datasets, where post-calibration results indicate improved trustworthiness. The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.

Subjective Logic (SL) enriches probabilistic logic by incorporating uncertainty and subjective belief ownership, enabling the expression of uncertainty about subjective beliefs. Unlike traditional probabilistic logics, SL 1) accommodates situations where different agents express beliefs about the same proposition, integrating the subjective nature and ownership of beliefs; and 2) addresses existing limitations in Dempster-Shafer Theory of evidence (DST), particularly in modelling trust transitivity. In modern computer systems, trust assessment extends beyond direct relationships to complex networks, necessitating the consideration of referral and direct trust relationships. This paper introduces a novel trust discount operator for referral edges in complex networks, addressing challenges in discounting trust across two and multiple referral edges. Through our empirical analysis, we demonstrate the effectiveness of the proposed operator and establish a relationship between path length and trustworthiness.