PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic
A new framework called PaTAS has been developed to model and propagate trust within neural networks using Subjective Logic. This system operates in parallel with standard neural computations, employing Trust Nodes and Trust Functions to assess and transmit trust levels related to inputs, parameters, and activations. PaTAS includes mechanisms for refining parameter reliability during training and for calculating instance-specific trust during inference, demonstrating its ability to provide interpretable trust estimates that complement accuracy metrics and highlight reliability issues in data. AI
IMPACT Introduces a novel method for quantifying and reasoning about trust in AI systems, crucial for safety-critical applications.