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Academic Articles & Research Papers
Showing 20 of 25 academic articles
Xiaoting Zhong, Brian J. Gallagher, Shusen Liu, Bhavya Kailkhura et al. • npj Computational Materials
(2022)
Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...
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DOI: https://doi.org/10.1038/s41524-022-00884-7
Sofia Morandini, Federico Fraboni, Mark Hall, Santiago Quintana-Amate et al. • Frontiers in Organizational Psychology
(2025)
The integration of AI technologies in aerospace manufacturing is significantly transforming critical operational processes, impacting decision-making, efficiency, and workflow optimization. Explainability in AI systems is essential to ensur...
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Open Access (DOAJ)
DOI: 10.3389/forgp.2025.1538438
Luciano Caroprese, Eugenio Vocaturo, Ester Zumpano • Intelligent Systems with Applications
(2022)
Artificial Intelligence algorithms are powerful in performing accurate predictions, but they are often considered black boxes as they do not provide any explanation about how outputs are derived from inputs or why a decision is taken. There...
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DOI: https://doi.org/10.1016/j.iswa.2022.200109
Feixue Yan, Sheng Wen, Surya Nepal, Cécile L. Paris et al. • International Journal of Intelligent Systems
(2022)
Machine learning (ML) techniques are increasingly important in cybersecurity, as they can quickly analyse and identify different types of threats from millions of events. In spite of the increasing number of possible applications of ML, suc...
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DOI: https://doi.org/10.1002/int.23088
Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, Keith T. Butler • Accounts of Materials Research
(2022)
High Resolution Image Download MS PowerPoint Slide Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experi...
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DOI: https://doi.org/10.1021/accountsmr.1c00244
Aditya Kuppa, Nhien‐An Le‐Khac • IEEE Transactions on Information Forensics and Security
(2021)
Machine Learning methods are playing a vital role in combating ever-evolving threats in the cybersecurity domain. Explanation methods that shed light on the decision process of black-box classifiers are one of the biggest drivers in the suc...
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DOI: https://doi.org/10.1109/tifs.2021.3117075
Nicola Capuano, Giuseppe Fenza, Vincenzo Loia, Claudio Stanzione • IEEE Access
(2022)
Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being’s daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn’t satisfy by design the principles...
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DOI: https://doi.org/10.1109/access.2022.3204171
Sargam Yadav, Abhishek Kaushik, Kevin McDaid • Applied AI Letters
(2026)
ABSTRACT Although artificial intelligence (AI) models have demonstrated great success and efficiency in automatically countering online hate speech and misogyny, they suffer from a lack of explainability and transparency. Explainable artifi...
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Open Access (DOAJ)
DOI: 10.1002/ail2.70028
Arlene Casey, Emma Davidson, Michael Poon, Hang Dong et al. • BMC Medical Informatics and Decision Making
(2021)
BACKGROUND: Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to r...
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DOI: https://doi.org/10.1186/s12911-021-01533-7
PMC: 8176715
Julia El Zini, Mariette Awad • ACM Computing Surveys
(2022)
Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such model...
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Open Access
DOI: https://doi.org/10.1145/3529755
Sumaya Mustafa, Mariwan Hama Saeed • Journal of Electrical Systems and Information Technology
(2025)
Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...
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Open Access (DOAJ)
DOI: 10.1186/s43067-025-00273-2
Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica • Discover Artificial Intelligence
(2024)
Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...
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Open Access (DOAJ)
DOI: 10.1007/s44163-024-00114-7
Boris Galitsky • Entropy
(2023)
It is well known that deep learning (DNN) has strong limitations due to a lack of explainability and weak defense against possible adversarial attacks. These attacks would be a concern for autonomous teams producing a state of high entropy ...
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Open Access (DOAJ)
DOI: 10.3390/e25111496
Shiv Singh, Neelu Jyothi Ahuja • Discover Computing
(2026)
Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...
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Open Access (DOAJ)
DOI: 10.1007/s10791-026-10123-y
Sergio Muñoz, Carlos Á. Iglesias • Big Data and Cognitive Computing
(2024)
The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...
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Open Access (DOAJ)
DOI: 10.3390/bdcc8100129
Rok Rajher, Mila Marinković, Polona Rus Prelog, Jure Žabkar • Scientific Reports
(2025)
Abstract Schizophrenia is a chronic and severe mental disorder that still relies on time-intensive, clinician-administered assessments. Although several automated approaches have been proposed to support diagnosis, these systems often lack ...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-33129-w
Ethem Alpaydın • The MIT Press eBooks
(2021)
A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product ...
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Open Access
DOI: https://doi.org/10.7551/mitpress/13811.001.0001
Chun Hian John Yap, Ziying Vanessa Lim • Annals, Academy of Medicine, Singapore
(2026)
Introduction: Artificial Intelligence (AI) is increasingly explored for medical education, and dermatology, with its visual diagnostic focus, holds promise for AI-enhanced learning. However, evidence on its educational effectiveness remains...
DOAJ
Open Access (DOAJ)
DOI: 10.47102/annals-acadmedsg.2026346
Sara S. Alhasan, Reem A. Alnanih • IEEE Access
(2025)
The increasing adoption of Artificial Intelligence (AI) in several industries has created a demand for user-centered explanations that align with how users think and understand concepts. This paper presents EXACT (EXplainable AI with Cognit...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3576234
Chen Xinyu, Hui Tianfang, Li Yanlin, Yang Haoyuan • ITM Web of Conferences
(2025)
As artificial intelligence (AI) is increasingly integrated into society, people are relying on it more and more, and higher requirements are put forward for the safety and ethical standards of AI. This article explores the development of ar...
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Open Access (DOAJ)
DOI: 10.1051/itmconf/20257004031