arXiv subjects

Koffi Ismael Ouattara

Publications and source records attributed to Koffi Ismael Ouattara.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.AI↗

arXiv2026

Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias

As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the dataset level. Prior work has used Subjective Logic to assess trustworthiness of individual data, but not to evaluate trustworthiness properties that emerge only at the level of the dataset as a whole. This paper introduces the first formal framework for assessing the trustworthiness of AI training datasets, enabling uncertainty-aware evaluations of global properties such as bias. Built on Subjective Logic, our approach supports trust propositions and quantifies uncertainty in scenarios where evidence is incomplete, distributed, and/or conflicting. We instantiate this framework on the trustworthiness property of bias, and we experimentally evaluate it based on a traffic sign recognition dataset. The results demonstrate that our method captures class imbalance and remains interpretable and robust in both centralized and federated contexts.

cs.LG↗

arXiv2025

Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory

Trustworthiness in neural networks is crucial for their deployment in critical applications, where reliability, confidence, and uncertainty play pivotal roles 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, disbelief, and uncertainty by clustering predicted probabilities and fusing opinions using appropriate fusion operators. We demonstrate the effectiveness of this approach through experiments on 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.

cs.LG↗