Trustworthiness has become a key requirement for deploying artificial intelligence in safety-critical applications, yet conventional metrics such as accuracy fail to capture uncertainty or the reliability of predictions, particularly under adversarial or degraded conditions. This paper introduces the Parallel Trust Assessment System (PaTAS), a framework for modeling and propagating trust in neural networks using Subjective Logic (SL). PaTAS operates in parallel with standard neural computation through Trust Nodes and Trust Functions that propagate input, parameter, and activation trust across the network, refining parameter trust during training (Parameter Trust Update) and returning a per-inference trust opinion, quantifying belief, disbelief, and uncertainty, on each prediction (Inference-Path Trust Assessment). Experiments on real-world and adversarial datasets show that these estimates are interpretable and stable and complement accuracy: PaTAS flags predictions that remain accurate yet rely on parameters learned from corrupted data, detects adversarially patched inputs without knowledge of the trigger, and audits training-label corruption without any test data. Compared with output-only uncertainty baselines and dedicated input- and feature-space detectors, out-of-distribution and corruption detection is carried by the conformity-derived input opinions PaTAS consumes, within a regime that a per-dataset diagnostic identifies in advance; trust propagation adds model-side signals these detectors lack, and the composed trust degrades gracefully where any single signal fails. The mechanism extends to convolutional networks. PaTAS thereby provides a foundation for transparent, quantifiable trust reasoning across the AI lifecycle.

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