explainable-ai

A fraud model looks at an insurance claim and returns a score of 0.23: low risk. A SHAP explanation lays out exactly why: no prior claims, a modest claim value, an unremarkable claimant profile. A human adjuster reads the explanation, agrees with it and signs off. Every box that explainable AI asks us to check has been checked. The claim is settled and closed. By any current standard for responsi…

Apply for funding for projects focused on speculative and high-risk fundamental research with the potential to deliver high reward and a step change in the explainability of future AI systems. You must be based at a UK research organisation eligible for UK Research and Innovation funding. UKRI-wide EPSRC

Scientific Reports, Published online: 07 August 2026; doi:10.1038/s41598-026-61426-5 Unpaired RGB-to-thermal learning for explainable diabetic-foot risk screening

Nature Medicine, Published online: 04 August 2026; doi:10.1038/s41591-026-04553-w Explainable AI, implemented for large language models that assist with dermatological diagnoses, has differing effects depending on whether the assistance is provided to primary care physicians or to lay users.

Scientific Reports, Published online: 31 July 2026; doi:10.1038/s41598-026-64406-x Explainable ensemble machine learning for dissolved oxygen prediction in a reservoir using SHAP and chord diagram analysis

Journal of Computer Science, Published online: 28 July 2026; doi:10.3844/jcssp.2026.2324.2333 In clinical tabular models, correlated biomarkers can make local explanations unstable even when predictive performance appears acceptable. This paper presents CA-LIME, a collinearity-aware extension ...

Scientific Reports, Published online: 16 July 2026; doi:10.1038/s41598-026-62284-x Exploring the non-linear impacts of environmental and anthropogenic factors on land surface temperature in southwestern Bangladesh using explainable AI

BackgroundFalls among older adults are a leading cause of morbidity and loss of independence. Wearable sensors combined with machine learning (ML) offer opportunities for objective fall risk evaluation, but low model transparency limits clinical adoption. Interpretable and explainable artificial intelligence (XAI) methods can address this constraint, yet their application in wearable sensor–based…

Machine learning models keep getting more accurate — and more opaque. A gradient-boosted ensemble or a deep neural net can outperform a human expert on a narrow task, but if nobody, including the engineers who built it, can say why it made a particular call, that accuracy comes with a hidden cost. That's the gap Explainable AI (XAI) tries to close: making a model's reasoning legible to the humans…

Accurate spatial measurement of aboveground biomass (AGB) is essential for assessing carbon stocks in the forest ecosystem. To enhance this estimation, integrating active and passive Earth Observation data with advanced machine learning techniques offers a promising approach. This study presents an integrated HybridEnsemble model with golden jackal optimization for AGB estimation and evaluates it…

Scientific Reports, Published online: 06 July 2026; doi:10.1038/s41598-026-60901-3 Explainable AI for employee turnover prediction: a SHAP-based intelligent analytics approach

Background and objectiveLung cancer is the top cause of cancer-related death, globally. The morphological complexity of tumours, intra-tumoural heterogeneity and limited interpretability of current imaging systems all contribute to the difficulty of early and reliable detection of tumours. This paper introduces LungCraft, a diagnostic framework composed of 3D medical modelling, quantitative radio…

Artificial intelligence systems are becoming increasingly powerful--but also harder to understand. A new study introduces eXplainable artificial intelligence - SHIELD (X-SHIELD), a regularization technique that improves both the performance and explainability of AI models by selectively hiding the least important features during training.

Termine, Alberto and Facchini, Alessandro and Ratti, Emanuele (2026) Explainable AI Models as Mediators. [Preprint]

Why did this workflow get cheaper last week? Why did support quality drop after a routing change? Was the failure caused by the model, the router, or the task decomposition? Most multi-model systems can route for cost. Very few can explain why a task was sent to a specific model, what tradeoff was made, and whether the cheaper path was actually justified. That is not just a research gap. It is an…