Enhancing explainability in pacu fish image segmentation using saliency maps and combined explainable AI methods by Juliana da C. Feitosa, Fabrício M. Batista, Juliana C.F. Catharino, Milena V. Freitas, Diogo T. Hashimoto, João Paulo Papa, José Remo F. Brega

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Explaining the undecidability of first-order logic by Timm Lampert, Anderson Nakano

“…Turing’s proof, however, merely predicts the non-existence of such solutions; it does not construct concrete examples that explain why specific attempts to solve the decision problem by pattern detection fail. …”
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Informing antimicrobial stewardship with explainable AI. by Massimo Cavallaro, Ed Moran, Benjamin Collyer, Noel D McCarthy, Christopher Green, Matt J Keeling

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Knowledge-infused and Explainable Malware Forensics by Neha Mohan Kumar, Sheikh Rabiul Islam

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Explainable Artificial Intelligence: Advancements and Limitations by Halil Ibrahim Aysel, Xiaohao Cai, Adam Prugel-Bennett

Subjects: “…explainable AI…”
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Managing self-explaining ambient applications by Börge Kordts, Lea C. Brandl, Andreas Schrader

“…Therefore, we extended a framework that utilizes self-explaining applications to generate user instructions dynamically that explain the control of these. …”
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Explaining and Considering the Theoretical Grounds of Pantheism by Mahdi Zandiyah

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Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

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Leveraging causality and explainability in digital agriculture by Ilias Tsoumas, Vasileios Sitokonstantinou, Georgios Giannarakis, Evagelia Lampiri, Christos Athanassiou, Gustau Camps-Valls, Charalampos Kontoes, Ioannis N. Athanasiadis

“…We propose a causal and explainable framework for enhancing digital agriculture, using pest management and its sustainable alternative, IPM, as a key example to highlight the contributions of causality and explainability. …”
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LISE: A Logic-Based Interactive Similarity Explainer for Clusters of RDF Data by Simona Colucci, Francesco Maria Donini, Verdiana Schena, Floriano Scioscia, Eugenio Di Sciascio

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Parkinson’s disease tremor explained by reflex loop changes by Dimitry Gorinevsky

“…Abstract Many Parkinson’s Disease tremor behaviors that were considered hard to interpret can be explained by control systems modeling of mid-level motor control. …”
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Explained theoretical models for strategic Management of Human Resources by Wahiba Mokadem

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Caelospermum versus Coelospermum in Rubiaceae (Gentianales): their etymologies explained by Brecht Verstraete, Elmar Robbrecht

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Quantum Tunnelling in Triplet Carbenes Explained by Instanton Theory by Meghna A. Manae, Jeremy O. Richardson

“…The observed temperature-dependent reactivity can thus be explained in terms of a crossover temperature where the mechanism switches. …”
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Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

“…Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. …”
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Explaining the Concept of Re-experiencing Abusive Relationships by Zinatsadat Mirpour, Mohammadali Rahmani, Shohreh Ghorbanshiroodi

“…Objective: This study aimed to explain the concept of re-experiencing harmful relationships. …”
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Explaining Institutional Change: Ambiguity, Agency and Power by Alicia Gómez

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Factors explaining adolescents’ digital skills in Europe by Maria Symeonaki, Glykeria Stamatopoulou, Dimitris Parsanoglou

“…The present paper utilizes raw data from the latest PISA 2022 survey to examine factors explaining digital skill scores of 15-year-old students in Europe in connection with several socio-demographic variables, namely gender, parental education, and migration background, as well as interest toward ICT, distraction from digital devices, and time spent on the internet. …”
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An Explainable Fuzzy Framework for Assessing Preeclampsia Classification by Matías Salinas, Daira Velandia, Leondry Mayeta-Revilla, Ayleen Bertini, Marvin Querales, Fabian Pardo, Rodrigo Salas

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