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The research aims to understand and communicate uncertainty in explainable AI to enhance model trustworthiness and user interaction.

March 26, 2026Open Access

Uncertainty in Explainable Artificial Intelligence

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The research aims to understand and communicate uncertainty in explainable AI to enhance model trustworthiness and user interaction.
Developed a mathematical framework for explaining uncertainty in AI models.
Compared uncertainty propagation methods to empirical data through testing.
Created a stochastic explainer and applied it to multimodal models in scientific workflows.
Studied the impact of uncertainty on human-AI collaboration using a human-in-the-loop approach.
Identified key sources of uncertainty in explainable AI systems.
Demonstrated improved transparency and reliability of explanations with the new framework.
Showed that understanding model uncertainty enhances user performance in unfamiliar tasks.

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This dissertation develops a framework addressing uncertainty in explanations, enhancing human-AI collaboration and transparency.

Teodor-Constantin Chiaburu (2026) studied this question.

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