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Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., & Holzinger, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118888 · Cited by 89 · see citing works

Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States

Kreps, S. E., George, J., Lushenko, P., & Rao, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288109 · Cited by 95 · see citing works

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar, A. I., Ahmad, I., Omeke, K. G., Öztürk, M., Öztürk, C., Abdel-Salam, A. M., Mollel, M. S., Abbasi, Q. H., Hussain, S., & Imran, M. A. (2023) · Drones · doi:10.3390/drones7030214 · Cited by 86 · see citing works

Machine learning activation energies of chemical reactions

Lewis‐Atwell, T., Townsend, P. A., & Grayson, M. N. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1593 · Cited by 85 · see citing works

Machine learning in project analytics: a data-driven framework and case study

Uddin, S., Ong, S., & Lu, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-19728-x · Cited by 85 · see citing works

Innovative approaches to addressing the tradeoff between interpretability and accuracy in ship fuel consumption prediction

Wang, H., Yan, R., Wang, S., & Zhen, L. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104361 · Cited by 84 · see citing works

Machine Learning for Understanding and Predicting Injuries in Football

Majumdar, A., Bakirov, R., Hodges, D., Scott, S., & Rees, T. (2022) · Sports Medicine - Open · doi:10.1186/s40798-022-00465-4 · Cited by 79 · see citing works

Recent Advances on Machine Learning Applications in Machining Processes

Aggogeri, F., Pellegrini, N., & Tagliani, F. L. (2021) · Applied Sciences · doi:10.3390/app11188764 · Cited by 78 · see citing works

AI literacy for ethical use of chatbot: Will students accept AI ethics?

Kajiwara, Y., & Kawabata, K. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100251 · Cited by 91 · see citing works

Interpretable machine learning methods for predictions in systems biology from omics data

Sidak, D., Schwarzerová, J., Weckwerth, W., & Waldherr, S. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.926623 · Cited by 77 · see citing works

Digital Twins for Condition and Fleet Monitoring of Aircraft: Toward More-Intelligent Electrified Aviation Systems

Sadeghi, A., Bellavista, P., Song, W., & Yazdani-Asrami, M. (2024) · IEEE Access · doi:10.1109/access.2024.3371902 · Cited by 89 · see citing works

Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare

Ennab, M., & Mcheick, H. (2022) · Diagnostics · doi:10.3390/diagnostics12071557 · Cited by 76 · see citing works

Content-based group recommender systems: A general taxonomy and further improvements

Pérez-Almaguer, Y., Yera, R., Alzahrani, A. A., & Martínez, L. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115444 · Cited by 74 · see citing works

Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles

Yeong, D. J., Panduru, K., & Walsh, J. (2025) · Sensors · doi:10.3390/s25030856 · Cited by 76 · see citing works

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Liewald, M., Bergs, T., Groche, P., Behrens, B., Briesenick, D., Müller, M., Niemietz, P., Kubik, C., & Müller, F. (2022) · Production Engineering · doi:10.1007/s11740-022-01115-0 · Cited by 65 · see citing works

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen, V. G., Rajamohan, S., Rudzki, K., Kozak, J., Sharma, P., Pham, N. D. K., Nguyen, P. Q. P., & Nguyễn, X. P. (2023) · Polish Maritime Research · doi:10.2478/pomr-2023-0020 · Cited by 63 · see citing works

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

Hamida, S. U., Chowdhury, M. J. M., Chakraborty, N. R., Biswas, K., & Sami, S. K. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110149 · Cited by 78 · see citing works

A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges

Zsidai, B., Hilkert, A., Kaarre, J., Narup, E., Senorski, E. H., Grassi, A., Ley, C., Longo, U. G., Herbst, E., Hirschmann, M. T., et al. (2023) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-023-00683-z · Cited by 65 · see citing works

Non-contact vehicle weighing method based on tire-road contact model and computer vision techniques

Kong, X., Zhang, J., Wang, T., Deng, L., & Cai, C. S. S. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109093 · Cited by 61 · see citing works

Recent advances in black box and white-box models for urban heat island prediction: Implications of fusing the two methods

Adilkhanova, I., Ngarambe, J., & Yun, G. Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112520 · Cited by 61 · see citing works

Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection

Aguilar, D. L., Medina‐Pérez, M. A., Loyola‐González, O., Choo, K. R., & Bucheli-Susarrey, E. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3148331 · Cited by 61 · see citing works

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Liu, L., Chen, X., Petinrin, O. O., Zhang, W., Rahaman, S., Tang, Z., & Wong, K. (2021) · Life · doi:10.3390/life11070638 · Cited by 58 · see citing works

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

Dzierżak, R., & Omiotek, Z. (2022) · Sensors · doi:10.3390/s22218189 · Cited by 60 · see citing works

Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)

Rožanec, J. M., Fortuna, B., & Mladenić, D. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.015 · Cited by 56 · see citing works

AI in Radiology: Navigating Medical Responsibility

Contaldo, M. T., Pasceri, G., Vignati, G., Bracchi, L., Triggiani, S., & Carrafiello, G. (2024) · Diagnostics · doi:10.3390/diagnostics14141506 · Cited by 54 · see citing works

Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data

Choi, Y., Park, S., & Lee, S. (2021) · Scientometrics · doi:10.1007/s11192-021-04001-1 · Cited by 53 · see citing works

Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system

Tal, A. S., Kuflik, T., & Kliger, D. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09623-4 · Cited by 52 · see citing works

Guaranteeing Correctness in Black-Box Machine Learning: A Fusion of Explainable AI and Formal Methods for Healthcare Decision-Making

Khan, N., Nauman, M. A., Almadhor, A. S., Akhtar, N., Alghuried, A., & Alhudhaif, A. (2024) · IEEE Access · doi:10.1109/access.2024.3420415 · Cited by 59 · see citing works

Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network

Jayaraman, R., Baskar, M., Annamalai, S., Kumar, M., Mishra, A., & Shrestha, R. (2023) · Electronics · doi:10.3390/electronics12020451 · Cited by 51 · see citing works

SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach

Ma, J., Liu, S., & Pei, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-24144-y · Cited by 51 · see citing works

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

Prado-Romero, M. A., Prenkaj, B., Stilo, G., & Giannotti, F. (2023) · ACM Computing Surveys · doi:10.1145/3618105 · Cited by 50 · see citing works

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

Sudriani, Y., Sebestyén, V., & Abonyi, J. (2023) · IEEE Access · doi:10.1109/access.2023.3263802 · Cited by 49 · see citing works

Earliest Possible Global and Local Interpretation of Students’ Performance in Virtual Learning Environment by Leveraging Explainable AI

Adnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S., & Alzahrani, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3227072 · Cited by 57 · see citing works

An Explainable Artificial Intelligence Model for Clustering Numerical Databases

Loyola‐González, O., Gutierrez-Rodríguez, A. E., Medina‐Pérez, M. A., Monroy, R., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & García-Borroto, M. (2020) · IEEE Access · doi:10.1109/access.2020.2980581 · Cited by 48 · see citing works

Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

Cornago, S., Tan, Y. S., Brondi, C., Ramakrishna, S., & Low, J. S. C. (2022) · Sustainability · doi:10.3390/su14116464 · Cited by 47 · see citing works

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology

Veritti, D., Rubinato, L., Sarao, V., Nardin, A. D., Foresti, G., & Lanzetta, P. (2023) · Graefe's Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-023-06245-4 · Cited by 30 · see citing works

Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

Hoepner, A. G. F., McMillan, D. G., Vivian, A. J., & Simen, C. W. (2020) · European Journal of Finance · doi:10.1080/1351847x.2020.1847725 · Cited by 43 · see citing works

Explainable AI for Soil Fertility Prediction

Chandra, H., Pawar, P. M., Elakkiya, R., Tamizharasan, P. S., Muthalagu, R., & Panthakkan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3311827 · Cited by 42 · see citing works

A novel grey box model for ship fuel consumption prediction adapted to complex navigating conditions

Fan, A., Wang, Y., Liu, Y., Yang, Z., & Hu, Z. (2025) · Energy · doi:10.1016/j.energy.2025.134436 · Cited by 42 · see citing works

Interpretable CEEMDAN-FE-LSTM-transformer hybrid model for predicting total phosphorus concentrations in surface water

Yao, J., Chen, S., & Ruan, X. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130609 · Cited by 41 · see citing works

Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology

Fuck, J. V. R., Cechinel, M. A. P., Neves, J., de Andrade, R. C., Tristão, R., Spogis, N., Riella, H. G., Soares, C., & Padoin, N. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141472 · Cited by 40 · see citing works

Explainable ResNet50 learning model based on copula entropy for cotton plant disease prediction

Askr, H., El-dosuky, M. A., Darwish, A., & Hassanien, A. E. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112009 · Cited by 40 · see citing works

A Review and Experimental Comparison of Multivariate Decision Trees

Cañete-Sifuentes, L., Monroy, R., & Medina‐Pérez, M. A. (2021) · IEEE Access · doi:10.1109/access.2021.3102239 · Cited by 40 · see citing works

A comprehensive review of fuzzy logic based interpretability and explainability of machine learning techniques across domains

Ouifak, H., & Idri, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130602 · Cited by 38 · see citing works

Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms

Rieg, T., Frick, J., Baumgartl, H., & Buettner, R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0243615 · Cited by 34 · see citing works

Do Not Trust Additive Explanations

Gosiewska, A., & Biecek, P. (2019) · arXiv · doi:10.48550/arxiv.1903.11420 · Cited by 34 · see citing works

Large-scale neurophysiology and single-cell profiling in human neuroscience

Lee, A. T., Chang, E. F., Paredes, M. F., & Nowakowski, T. J. (2024) · Nature · doi:10.1038/s41586-024-07405-0 · Cited by 33 · see citing works

An Introduction to Machine Learning for Panel Data

Chen, J. M. (2021) · International Advances in Economic Research · doi:10.1007/s11294-021-09815-6 · Cited by 34 · see citing works

Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics

Ramaswami, G. S., Sušnjak, T., & Mathrani, A. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040105 · Cited by 33 · see citing works

Adaptive thermal load prediction in residential buildings using artificial neural networks

Fouladfar, M. H., Soppelsa, A., Nagpal, H., Fedrizzi, R., & Franchini, G. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107464 · Cited by 33 · see citing works

Achieving Expert-Level Interpretation of Serum Protein Electrophoresis through Deep Learning Driven by Human Reasoning

Chabrun, F., Dieu, X., Ferré, M., Gaillard, O., Mery, A., de la Barca, J. M. C., Taisne, A., Urbanski, G., Reynier, P., & Mirebeau‐Prunier, D. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab133 · Cited by 33 · see citing works

When facial recognition does not ‘recognise’: erroneous identifications and resulting liabilities

Raposo, V. L. (2023) · AI & Society · doi:10.1007/s00146-023-01634-z · Cited by 32 · see citing works

Paving the road towards automated homogeneous catalyst design

Kalikadien, A. V., Mirza, A., Hossaini, A. N., Sreenithya, A., & Pidko, E. A. (2024) · ChemPlusChem · doi:10.1002/cplu.202300702 · Cited by 35 · see citing works

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

Lovrić, M., Banić, I., Lacić, E., Kern, R., Pavlović, K., & Turkalj, M. (2021) · Children · doi:10.3390/children8050376 · Cited by 32 · see citing works

Explainable Sentiment Analysis: A Hierarchical Transformer-Based Extractive Summarization Approach

Bacco, L., Cimino, A., Dell’Orletta⋄, F., & Merone, M. (2021) · Electronics · doi:10.3390/electronics10182195 · Cited by 33 · see citing works

ML-based vehicle downtime reduction: A case of air compressor failure detection

Panda, C., & Singh, T. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106031 · Cited by 32 · see citing works

Interpretable belief rule base for safety state assessment with reverse causal inference

Yin, X., He, W., Cao, Y., Zhou, G., & Li, H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119748 · Cited by 32 · see citing works

Explainable AI: roles and stakeholders, desirements and challenges

Hoffman, R. R., Mueller, S. T., Klein, G. A., Jalaeian, M., & Tate, C. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1117848 · Cited by 31 · see citing works

Applications and challenges of AI-based algorithms in the COVID-19 pandemic

Khemasuwan, D., & Colt, H. G. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2020-000648 · Cited by 31 · see citing works

Machine learning role playing game: Instructional design of AI education for age-appropriate in K-12 and beyond

Kajiwara, Y., Matsuoka, A., & Shinbo, F. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100162 · Cited by 49 · see citing works

From biological data to oscillator models using SINDy

Prokop, B., & Gelens, L. (2024) · iScience · doi:10.1016/j.isci.2024.109316 · Cited by 29 · see citing works

Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach

Yao, L., & He, N. (2023) · Scientometrics · doi:10.1007/s11192-023-04736-z · Cited by 29 · see citing works

An integrated multi-modeling framework to estimate potential rice and energy production under an agrivoltaic system

Kim, S., Kim, S., & An, K.-N. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108157 · Cited by 29 · see citing works

A systematic review on modelling the thermal environment of vehicle cabins

Zhao, L., Zhou, Q., & Wang, Z. (2024) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2024.124142 · Cited by 29 · see citing works

An explainable deep learning platform for molecular discovery

Wong, F. J., Omori, S., Li, A., Krishnan, A., Lach, R. S., Rufo, J., Wilson, M. Z., & Collins, J. J. (2024) · Nature Protocols · doi:10.1038/s41596-024-01084-x · Cited by 28 · see citing works

Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features

Kerz, E., Qiao, Y., Zanwar, S., & Wiechmann, D. (2022) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2022.wassa-1.17 · Cited by 29 · see citing works

Predicting shale mineralogical brittleness index from seismic and elastic property logs using interpretable deep learning

Lee, J., & Lumley, D. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.111231 · Cited by 29 · see citing works

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

Mousavi, S. P., Nakhaei-Kohani, R., Atashrouz, S., Hadavimoghaddam, F., Abedi, A., Hemmati‐Sarapardeh, A., & Mohaddespour, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34193-w · Cited by 29 · see citing works

Alzheimer’s Disease Detection from Spontaneous Speech Through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models

Qiao, Y., Yin, X., Wiechmann, D., & Kerz, E. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1415 · Cited by 37 · see citing works

A framework proposal for machine learning-driven agent-based models through a case study analysis

Turgut, Y., & Bozdag, E. (2022) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2022.102707 · Cited by 28 · see citing works

High-Impedance Fault Detection Methodology Using Time–Frequency Spectrum and Transfer Convolutional Neural Network in Distribution Networks

Guo, M., Guo, Z., Gao, J., & Chen, D.-Y. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3281826 · Cited by 27 · see citing works

Data-driven causal inference of process-structure relationships in nanocatalysis

Ting, J. Y. C., & Barnard, A. S. (2022) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2022.100818 · Cited by 27 · see citing works

Binary Classification for Enhancing Explainability of Combined Black-Box and White-Box Models

Tanaka, S., & Iima, H. (2025) · International Conference on Neural Information Processing · doi:10.24135/iconip16

Comparing Explanations from Glass-Box and Black-Box Machine-Learning Models

Kuk, M., Bobek, S., & Nalepa, G. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08757-8_55 · Cited by 6 · see citing works

Benchmarking and survey of explanation methods for black box models

Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 353 · see citing works

HOW MUCH IS THE BLACK BOX? THE VALUE OF EXPLAINABILITY IN MACHINE LEARNING MODELS

Wanner, J., Herm, L.-V., & Janiesch, C. (2020) · Journal of the Association for Information Systems · Cited by 16 · see citing works

Explainable Black Box Models

Mulder, W. D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16072-1_42 · Cited by 2 · see citing works