explainable-ai

Crop recommendation is a vital part of precision agriculture as it helps farmers choose appropriate crops according to the nutrient profile and environmental conditions. This paper presents a crop recommendation framework in which Multi-Layer Perceptron (MLP), XGBoost, and Tab Transformer are first evaluated as baseline prediction models, followed by the proposed Krill Herd Optimization (KHO)-bas…

Publication date: Available online 2 September 2026 Source: Computers in Human Behavior Author(s): Felix Kares, Timo Speith, Hanwei Zhang, Markus Langer

Scientific Reports, Published online: 03 September 2026; doi:10.1038/s41598-026-68250-x Explainable machine learning predicts adolescent cognitive development from sport engagement in a national longitudinal survey

Scientific Reports, Published online: 03 September 2026; doi:10.1038/s41598-026-69689-8 An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay

This article examines a study of the normativity revealed when creators use Stable Diffusion and the changes that occur in the creative process.

This paper seeks to explore the history of artificial intelligence (AI) and explainable AI (XAI), including its recent role in a multitude of disciplines, such as healthcare, finance and law. The paper seeks to explore both the advantages and disadvantages of deploying XAI in the concerned fields and to visualise its use in the future.

_Synthese_. forthcomingMachine learning models based on artificial neural networks have been increasingly used in scientific research and the public domain. These models are notoriously opaque and may conceal caveats in important tasks. Explainable artificial intelligence (XAI) develops network interpretation strategies that reveal how these models work. However, the situation of XAI does not mee…

By combining deterministic engines with explainable AI, the industry can accelerate signoff, improve manufacturing outcomes and give engineering teams greater confidence in every decision from design through production. The post Welcome to the era of trustworthy AI for IC signoff and manufacturing appeared first on Electronics Weekly .

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.

[Accepted to the 2026 Philosophy of Science Association meeting, presumptively to appear in the conference proceedings edition of Philosophy of Science] Deep-learning-based AI systems are notoriously opaque. Our tools for coping with this opacity include “explainable AI” (XAI) methods such as LIME, which aims to explain a given AI output by providing a linear model intended to locally approximate…

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 ...