A decision-space model explains context-specific decision-making by Dirk W. Beck, Cory N. Heaton, Luis D. Davila, Lara I. Rakocevic, Sabrina M. Drammis, Danil Tyulmankov, Atanu Giri, Shreeya Umashankar Beck, Qingyang Zhang, Michael Pokojovy, Kenichiro Negishi, Alexis A. Salcido, Neftali F. Reyes, Andrea Y. Macias, Serina A. Batson, Paulina Vara, Raquel J. Ibáñez Alcalá, Safa B. Hossain, Graham L. Waller, Laura E. O’Dell, Travis M. Moschak, Ki A. Goosens, Alexander Friedman

“…We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. …”
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Segmenting female students' perceptions about Fintech using Explainable AI by Christos Adam, Christos Adam

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Can human movements explain heterogeneous propagation of dengue fever in Cambodia? by Magali Teurlai, Rekol Huy, Bernard Cazelles, Raphaël Duboz, Christophe Baehr, Sirenda Vong

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Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction by Nihad Brahimi, Huaping Zhang, Zahid Razzaq

Subjects: “…explainable spatio-temporal inference network…”
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Explainable Reinforcement and Causal Learning for Improving Trust to 6G Stakeholders by Miguel Arana-Catania, Amir Sonee, Abdul-Manan Khan, Kavan Fatehi, Yun Tang, Bailu Jin, Anna Soligo, David Boyle, Radu Calinescu, Poonam Yadav, Hamed Ahmadi, Antonios Tsourdos, Weisi Guo, Alessandra Russo

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Toward long-range ENSO prediction with an explainable deep learning model by Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang

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Explaining and Prioritizing Supplier Evaluation Indicators with a Meta-Synthesis Approach by Sayyed Mohammad Reza Davoodi, Mehdi Papahn Shoushtari, Rasoul Bahalou Houreh, Hamid Reza Rezaei

“…The present study aims to explain and prioritize the indices of supplier assessment with a meta-synthesis approach.Methodology: In this article, the most effective evaluation indicators of suppliers are examined. …”
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SPARC: A Human-in-the-Loop Framework for Learning and Explaining Spatial Concepts by Brendan Young, Derek T. Anderson, James Keller, Frederick Petry, Chris J. Michael

“…In this article, we introduce a novel framework for learning spatial concepts within a human-in-the-loop (HITL) context, highlighting the critical role of explainability in AI systems. By incorporating human feedback, the approach enhances the learning process, making it particularly suitable for applications where user trust and interpretability are essential, such as AiTR. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

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Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy by Xiangyu Shao, Wenjun Yan, Chaoying Yan, Wen Zhao, Yixuan Wang, Xia Shi, Hongchang Dong, Tianjiang Li, Junpo Yu, Peng Zuo, Zeyu Zhou, Jiming Jin

“…Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. …”
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Explainable surrogate modeling for predicting temperature separation performance of the vortex tube by Hyo Beom Heo, Jun Ho Lee, Jeong Won Yoon, Sangseok Yu, Byoung Jae Kim, Seokyeon Im, Seung Hwan Park

“…Furthermore, due to the complex internal flow field within the vortex tube, no theoretical formula has been established to explain the temperature separation phenomenon. Therefore, this study aims to develop a general surrogate model for predicting the performance of the vortex tube using symbolic regression, a representative white-box machine learning model. …”
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Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries by Xinyan Liu, Hong-Jie Peng

“…In a recent Joule study, Zhou, Li, and colleagues proposed an explainable-AI-based approach to intelligently design catalysts adaptive to diverse local chemical environments in batteries, achieving exceptional catalytic and battery performance.…”
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Impacts of Financial Inclusion on Micro-Businesses: Factors Explaining Business Growth by David Rodriguez-Gonzalez, Andres Garcia-Suaza, Alexander Sarango-Iturralde, Juan Diego Mayorga, Alvaro Pretel-Meneses, Soraya Husain-Talero, Lina Zarama, Juan Camilo Urbano, Daniel Giron, Natalia Medina, Aura Barberan

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TARGE: large language model-powered explainable hate speech detection by Muhammad Haseeb Hashir, Memoona, Sung Won Kim

“…These rationales are subsequently integrated into specialized classifiers designed to perform explainable content moderation. We rigorously evaluate our methodology on multiple benchmark English-language social media datasets. …”
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Explaining When Deep Learning Models Are Better for Time Series Forecasting by Martín Solís, Luis-Alexander Calvo-Valverde

“…There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. …”
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Explaining the framework of sustainable competitiveness in the direction of spatial development of Arak District by Mina Eftekharnia, Keramatollah Ziari, Majid Naderi

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Phenolic Metabolism Explains Bitterness and Pungency of Extra Virgin Olive Oils by Sonia Tomé-Rodríguez, Francisco Barba-Palomeque, Mónica Calderón-Santiago, José María Penco-Valenzuela, Feliciano Priego-Capote

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Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review by Daniele Pelosi, Diletta Cacciagrano, Marco Piangerelli

Subjects: “…explainability…”
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Explaining domination and emancipation in Islamic philosophy by emphasizing on the thought of Ayatollah Javadi by Mohamaddavod Modaghegh, حسین شرف الدین

“…Explaining domination and liberation in Islamic wisdom by emphasizing the thought of Ayatollah Javadi is the main issue of the article. …”
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Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence by Paolo Sorino, Angela Lombardi, Domenico Lofù, Tommaso Colafiglio, Antonio Ferrara, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia

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