SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

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A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory by Mengyuan Xu, Haoxuan Yang, Annan Hu, Lee Heng, Linyi Li, Ning Yao, Gang Liu

“…However, their purely “black box” algorithms suffer from a lack of interpretability, whereas methods based solely on physical mechanisms often underperform in complex scenarios. …”
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Multiscale Structural Information-Based Laplacian Generative Adversarial Network Representation Learning by Yan Liu, Xi Chen, Zheng Lu, Ziyue Wu

“…Deep learning-based algorithms are popular owing to their good performance to learn network representations, but they lack sufficient interpretability as closed boxes. …”
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Adopting Land Cover Standards for Sustainable Development in Ghana: Challenges and Opportunities by Elisha Njomaba, Fatima Mushtaq, Raymond Kwame Nagbija, Silas Yakalim, Ben Emunah Aikins, Peter Surovy

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Generative Local Interpretable Model-Agnostic Explanations by Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari

“…Despite their success, when used for decision-making, AI solutions have a significant drawback: transparency. The lack of transparency behind their behaviors, particularly in complex state-of-the-art machine learning algorithms, leaves users with little understanding of how these models make specific decisions. …”
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Logic, Dilemma and Realization of Digital Transformation of Classroom Teaching Assessment by LIN Ying, TIAN Yingqian, WANG Jinrong

“…The dilemma lies in the insufficient digital literacy of the subjects, the constraints of algorithmic black box on assessment methods, lack of standards for assessment contents, and ethical misconduct in feedback data. …”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

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Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review by Yasmin Abdelaal, Michaël Aupetit, Abdelkader Baggag, Dena Al-Thani

“…However, intricate machine learning and deep learning algorithms often lead to the development of “black box” models, which lack transparency and comprehensibility for medical professionals and end users. …”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Industrial-scale prediction of cement clinker phases using machine learning by Sheikh Junaid Fayaz, Néstor Montiel-Bohórquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan

“…Through post hoc explainable algorithms, we interpret the hierarchical relationships between clinker oxides and phase formation, providing insights into the functioning of an otherwise black-box model. …”
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An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set by Jinyuan Li, Wei He, Hailong Zhu

“…However, most current models are black-box models, such as deep learning models such as deep neural networks. …”
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A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity by Vincent Zibi Mohale, Ibidun Christiana Obagbuwa

“…Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a “black box” effect that can hinder the analysts’ understanding of their decision-making processes. …”
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The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors by Yixin Wang, Zongtao Hu, Hongzhi Wang

“…However, radiomic analyses often function as a ‘black box’ due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. …”
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Developing a Unified Framework for PMSM Speed Regulation: Active Disturbance Rejection Control via Generalized PI Control by Huanzhi Wang, Yuefei Zuo, Chenhao Zhao, Christopher H. T. Lee

“…With the growing demand for advanced control algorithms in permanent magnet synchronous motor (PMSM) speed regulation, active disturbance rejection control (ADRC) has garnered significant attention for its simplicity and effectiveness as an alternative to traditional proportional-integral (PI) controllers. …”
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OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

“…However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. …”
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Indirect determination of hemoglobin A2 reference intervals in Pakistani infants using data mining by Muhammad Shariq Shaikh, Sibtain Ahmed, Saba Farrukh, Shahnawaz Bayunus

“…RIs were computed using an indirect KOSMIC algorithm, which assumes non-pathologic samples follow a Gaussian distribution after Box-Cox transformation. …”
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AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses by Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer, Rakesh Heer, Kabita Adhikari

“…It equips researchers with a deeper understanding of the intricacies of the ML algorithms employed. Actionable insights are provided for refining algorithms, optimising multimodal data utilisation, and bridging the gap between predictive accuracy and clinical utility. …”
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Advancing clinical biochemistry: addressing gaps and driving future innovations by Haiou Cao, Enwa Felix Oghenemaro, Amaliya Latypova, Amaliya Latypova, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Gaffar Sarwar Zaman, Anita Devi

“…However, concerns regarding algorithmic bias, data privacy, lack of transparency in decision-making (“black box” models), and over-reliance on automated systems pose significant challenges that must be addressed for responsible AI integration. …”
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Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK by Saoudi CE Nouis, Victoria Uren, Srushti Jariwala

“…Transparency emerged as another major theme, with clinicians emphasizing the difficulty of trusting “black box” models that lack clear rationale or interpretability—particularly for rare or complex cases. …”
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AI-powered literature search: some observations and concerns by Farooq Azam Rathore, Fareeha Farooq

“…A lack of understanding of the mechanisms and algorithms can negatively influence their ability to critically evaluate the relevance and reliability of the AI-generated outputs. …”
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