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New framework models and propagates trust in neural networks

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON This is a research paper introducing a new framework for AI safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework models and propagates trust in neural networks

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This is a research paper introducing a new framework for AI safety. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Koffi Ismael Ouattara, Ioannis Krontiris, Theo Dimitrakos, Dennis Eisermann, Houda Labiod, Frank Kargl ·

    PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic

    arXiv:2511.20586v4 Announce Type: replace Abstract: Trustworthiness has become a key requirement for the deployment of artificial intelligence systems in safety-critical applications. Conventional evaluation metrics, such as accuracy and precision, fail to appropriately capture u…

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