Toward Human-Understandable, Explainable AI

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

Methods for interpreting and understanding deep neural networks

Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works

European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”

Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · see citing works

A Hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots

Hagras, H. (2004) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2004.832538 · Cited by 1,021 · see citing works

Uncertain Rule-Based Fuzzy Systems

Mendel, J. M. (2017) · Springer eBooks · doi:10.1007/978-3-319-51370-6 · Cited by 597 · see citing works

A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data

Sanz, J., Bernardo, D., Herrera, F., Bustince, H., & Hagras, H. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2014.2336263 · Cited by 171 · see citing works

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Antonelli, M., Bernardo, D., Hagras, H., & Marcelloni, F. (2016) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2578341 · Cited by 107 · see citing works

A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop

Holzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2019) · Creative Mathematics and Informatics · doi:10.37193/cmi.2019.02.04 · Cited by 86 · see citing works

Untitled work

(2017) · arXiv (Cornell University) · Cited by 86 · see citing works

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction InvariancePreprint

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05817 · Cited by 60 · see citing works

How AI detectives are cracking open the black box of deep learning

(2017) · AAAS Articles DO Group · doi:10.1126/science.aan7059 · Cited by 59 · see citing works

Explainable Artificial Intelligence

Pan, Z., & Mishra, P. (2023) · Springer eBooks · doi:10.1007/978-3-031-46479-9_2 · Cited by 5 · see citing works

Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings

Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048 · see citing works

Explainable artificial intelligence: a comprehensive review

Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works

Outlook on human-centric manufacturing towards Industry 5.0

Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X. W., Wang, L., Qin, Z., & Bao, J. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.02.001 · Cited by 725 · see citing works

Evaluating the quality of machine learning explanations: a survey on methods and metrics

Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works

Trustworthy artificial intelligence

Thiebes, S., Lins, S., & Sunyaev, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00441-4 · Cited by 595 · see citing works

The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work

Bankins, S., & Formosa, P. (2023) · Journal of Business Ethics · doi:10.1007/s10551-023-05339-7 · Cited by 554 · see citing works

Exploring artificial intelligence adoption in public organizations: a comparative case study

Neumann, O., Guirguis, K., & Steiner, R. (2024) · Public Management Review · doi:10.1080/14719037.2022.2048685 · Cited by 451 · see citing works

Explainable artificial intelligence (XAI) in finance: a systematic literature review

Černevičienė, J., & Kabašinskas, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10854-8 · Cited by 458 · see citing works

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Gao, Y., & Liu, H. (2022) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-01-2022-0023 · Cited by 357 · see citing works

Supporting Human-AI Teams:Transparency, explainability, and situation awareness

Endsley, M. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107574 · Cited by 332 · see citing works

Artificial intelligence in dentistry-a review

Ding, H., Wu, J., Zhao, W., Matinlinna, J. P., Burrow, M. F., & Tsoi, J. K. H. (2023) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2023.1085251 · Cited by 297 · see citing works

Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review

Mazhar, T., Irfan, H. M., Haq, I., Ullah, I., Ashraf, M., Shloul, T. A., Ghadi, Y. Y., Imran, I., & Elkamchouchi, D. H. (2023) · Electronics · doi:10.3390/electronics12010242 · Cited by 265 · see citing works

AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context

Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10223-8 · Cited by 249 · see citing works

AI business model: an integrative business approach

Mishra, S., & Tripathi, A. (2021) · Journal of Innovation and Entrepreneurship · doi:10.1186/s13731-021-00157-5 · Cited by 188 · see citing works

DISTILLER: Encrypted traffic classification via multimodal multitask deep learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.102985 · Cited by 185 · see citing works

XAI Meets Mobile Traffic Classification: Understanding and Improving Multimodal Deep Learning Architectures

Nascita, A., Montieri, A., Aceto, G., Ciuonzo, D., Persico, V., & Pescapè, A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3098157 · Cited by 182 · see citing works

Knowledge graph construction and application in geosciences: A review

Ma, X. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105082 · Cited by 174 · see citing works

How the different explanation classes impact trust calibration: The case of clinical decision support systems

Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102941 · Cited by 170 · see citing works

Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human

Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Prendin, F., Pavan, J., Cappon, G., Favero, S. D., Sparacino, G., & Facchinetti, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-44155-x · Cited by 155 · see citing works

Explainable artificial intelligence for cybersecurity: a literature survey

Charmet, F., Tanuwidjaja, H. C., Ayoubi, S., Gimenez, P.-F., Han, Y., Jmila, H., Blanc, G., Takahashi, T., & Zhang, Z. (2022) · Annals of Telecommunications · doi:10.1007/s12243-022-00926-7 · Cited by 167 · see citing works

Transparency in persuasive technology, immersive technology, and online marketing: Facilitating users’ informed decision making and practical implications

Wang, R., Bush‐Evans, R., Arden-Close, E. J., Bolat, E., McAlaney, J., Hodge, S., Thomas, S., & Phalp, K. T. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107545 · Cited by 150 · see citing works

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

Shah, S. B., Iqbal, M., Aziz, Z., Rana, T. A., Khalid, A., Cheah, Y., & Arif, M. S. (2022) · Applied Sciences · doi:10.3390/app12157882 · Cited by 148 · see citing works

Toward effective mobile encrypted traffic classification through deep learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.036 · Cited by 147 · see citing works

A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome

Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., Bhandage, V. A., & Hegde, G. K. (2023) · Applied System Innovation · doi:10.3390/asi6020032 · Cited by 138 · see citing works

Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

Zhang, K., Zhang, J. J., Xu, P., Gao, T., & Gao, W. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3096824 · Cited by 137 · see citing works

A bird's-eye view of deep learning in bioimage analysis

Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works

Integrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security

Chen, H.-Y., Sharma, K., Sharma, C. B., & Sharma, S. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100350 · Cited by 126 · see citing works

Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation

Molina, M. D., & Sundar, S. S. (2024) · New Media & Society · doi:10.1177/14614448221103534 · Cited by 124 · see citing works

AI explainability and governance in smart energy systems: A review

Alsaigh, R., Mehmood, R., & Katib, I. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1071291 · Cited by 121 · see citing works

Perinatal health predictors using artificial intelligence: A review

Ramakrishnan, R., Rao, S., & He, J. (2021) · Women s Health · doi:10.1177/17455065211046132 · Cited by 119 · see citing works

A survey on artificial intelligence assurance

Batarseh, F. A., Freeman, L., & Huang, C. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00445-7 · Cited by 117 · see citing works

Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

Varshney, A. K., & Torra, V. (2023) · International Journal of Fuzzy Systems · doi:10.1007/s40815-023-01534-w · Cited by 100 · see citing works

Personas for Artificial Intelligence (AI) an Open Source Toolbox

Holzinger, A., Kargl, M., Kipperer, B., Regitnig, P., Plass, M., & Müller, H. (2022) · IEEE Access · doi:10.1109/access.2022.3154776 · Cited by 99 · see citing works

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

Sahoh, B., & Choksuriwong, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04594-w · Cited by 98 · see citing works

OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild

Le, T.-N., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00996 · Cited by 97 · see citing works

Critical Thinking About Explainable AI (XAI) for Rule-Based Fuzzy Systems

Mendel, J. M., & Bonissone, P. P. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3079503 · Cited by 95 · see citing works

Organizations as Artificial Intelligences: The Use of Artificial Intelligence Analogies in Organization Theory

Csaszar, F. A., & Steinberger, T. (2021) · Academy of Management Annals · doi:10.5465/annals.2020.0192 · Cited by 95 · see citing works

Machine learning for an explainable cost prediction of medical insurance

Orji, U. E., & Ukwandu, E. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100516 · Cited by 90 · see citing works

Explanation of Machine-Learning Solutions in Air-Traffic Management

Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021) · Aerospace · doi:10.3390/aerospace8080224 · Cited by 89 · see citing works

Explainability in supply chain operational risk management: A systematic literature review

Nimmy, S. F., Hussain, F. K., Chakrabortty, R. K., Hussain, F. K., & Saberi, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107587 · Cited by 89 · see citing works

A review of power system protection and asset management with machine learning techniques

Aminifar, F., Abedini, M., Amraee, T., Jafarian, P., Samimi, M. H., & Shahidehpour, M. (2021) · Energy Systems · doi:10.1007/s12667-021-00448-6 · Cited by 86 · see citing works

Autonomous learning for fuzzy systems: a review

Gu, X., Han, J., Shen, Q., & Angelov, P. P. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10355-6 · Cited by 86 · see citing works

Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting

Carta, S., Consoli, S., Piras, L., Podda, A. S., & Recupero, D. R. (2021) · IEEE Access · doi:10.1109/access.2021.3059960 · Cited by 85 · see citing works

Generalizing AI: Challenges and Opportunities for Plug and Play AI Solutions

Ridhawi, I. A., Otoum, S., Aloqaily, M., & Boukerche, A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000371 · Cited by 84 · see citing works

Characterization and Prediction of Mobile-App Traffic Using Markov Modeling

Aceto, G., Bovenzi, G., Ciuonzo, D., Montieri, A., Persico, V., & Pescapè, A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3051381 · Cited by 83 · see citing works

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi, C., Bogliolo, A., & Montagna, S. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02582-4 · Cited by 81 · see citing works

IoT-based safety monitoring from the perspective of construction site workers

Häikiö, J., Kallio, J., Mäkelä, S.-M., & Keränen, J. S. (2020) · International Journal of Occupational and Environmental Safety · doi:10.24840/2184-0954_004.001_0001 · Cited by 77 · see citing works

Intelligent Manufacturing From the Perspective of Industry 5.0: Application Review and Prospects

Lei, Z., Shi, J., Luo, Z., Cheng, M., & Wan, J. (2024) · IEEE Access · doi:10.1109/access.2024.3496697 · Cited by 72 · see citing works

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

Yang, L.-H., Liu, J., Ye, F.-F., Wang, Y., Nugent, C., Wang, H., & Martínez, L. M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107805 · Cited by 69 · see citing works

Physical Layer Authentication and Security Design in the Machine Learning Era

Hoang, T. M., Vahid, A., Tuan, H. D., & Hanzo, L. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3363639 · Cited by 67 · see citing works

Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues

Parziale, A., Senatore, R., Cioppa, A. D., & Marcelli, A. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101984 · Cited by 66 · see citing works

Explainable Artificial Intelligence (XAI): An Engineering PerspectivePreprint

Hussain, F. A., Hussain, R., & Hossain, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03613 · Cited by 66 · see citing works

Explainable Artificial Intelligence for Medical Applications: A Review

Sun, Q., Akman, A., & Schuller, B. W. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3709367 · Cited by 64 · see citing works

Deep learning-based sewer defect classification for highly imbalanced dataset

Dang, L. M., Kyeong, S., Li, Y., Wang, H., Nguyen, T. N., & Moon, H. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107630 · Cited by 62 · see citing works

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Hajilounezhad, T., Bao, R., Palaniappan, K., Bunyak, F., Calyam, P. P., & Maschmann, M. R. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00603-8 · Cited by 60 · see citing works

On the Commoditization of Artificial Intelligence

Abonamah, A. A., Tariq, M. U., & Shilbayeh, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.696346 · Cited by 58 · see citing works

An Interpretable Artificial Intelligence Based Smart Agriculture System

Sabrina, F., Sohail, S., Farid, F., Jahan, S., Ahamed, F., & Gordon, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026363 · Cited by 57 · 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-Based Robustness Optimization Approach for Age-Related Macular Degeneration Detection Based on Medical IOT Systems

Wang, H., Chong, K. K. L., Lin, Z., Yu, X., & Pan, Y. F. (2023) · Electronics · doi:10.3390/electronics12122697 · Cited by 55 · see citing works

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI

Tariq, M., Ali, U., Abbas, S., Hassan, S. G., Naqvi, R. A., Khan, M. A., & Jeong, D. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1402835 · Cited by 56 · see citing works

AI-Powered Smart Grids in the 6G Era: A Comprehensive Survey on Security and Intelligent Energy Systems

Sanjalawe, Y. K., Al-E’mari, S., Fraihat, S., Makhadmeh, S. N., & Alzubi, E. A. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3609144 · Cited by 53 · see citing works

A Naturalistic Investigation of Trust, AI, and Intelligence Work

Dorton, S. L., & Harper, S. B. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221103718 · Cited by 52 · see citing works

A self-training hierarchical prototype-based approach for semi-supervised classification

Gu, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.018 · Cited by 51 · see citing works

Artificial agents’ explainability to support trust: considerations on timing and context

Papagni, G., de Pagter, J., Zafari, S., Filzmoser, M., & Koeszegi, S. T. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01462-7 · Cited by 52 · see citing works

Need for Interpretable Student Performance Prediction

Chitti, M., Chitti, P., & Jayabalan, M. (2020) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese51703.2020.9450735 · Cited by 49 · see citing works

Information geometry for multiparameter models: new perspectives on the origin of simplicity

Quinn, K. N., Abbott, M. C., Transtrum, M. K., Machta, B. B., & Sethna, J. P. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/aca6f8 · Cited by 48 · see citing works

Human-centered AI and robotics

Doncieux, S., Chatila, R., Straube, S., & Kirchner, F. (2022) · AI Perspectives · doi:10.1186/s42467-021-00014-x · Cited by 48 · see citing works

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice

Shang, Y., Tian, Y., Zhou, M., Zhou, T., Lyu, K., Wang, Z., Ran, X., Liang, T., Zhu, S., & Li, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3085003 · Cited by 48 · see citing works

Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective

Dalvi‐Esfahani, M., Mosharaf‐Dehkordi, M., Leong, L. W., Ramayah, T., & Kanaan-Jebna, A. M. J. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122768 · Cited by 46 · see citing works

Evaluating significant features in context‐aware multimodal emotion recognition with XAI methods

Khalane, A., Makwana, R., Shaikh, T., & Ullah, A. (2023) · Expert Systems · doi:10.1111/exsy.13403 · Cited by 46 · see citing works

Legal responsibility for errors caused by artificial intelligence (AI) in the public sector

AL-Dulaimi, A. O. M., & Mohammed, M. A.-A. W. (2025) · International Journal of Law and Management · doi:10.1108/ijlma-08-2024-0295 · Cited by 45 · see citing works

Packet-level prediction of mobile-app traffic using multitask Deep Learning

Montieri, A., Bovenzi, G., Aceto, G., Ciuonzo, D., Persico, V., & Pescapè, A. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108529 · Cited by 45 · see citing works

Self-organizing fuzzy inference ensemble system for big streaming data classification

Gu, X., Angelov, P. P., & Zhao, Z. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106870 · Cited by 43 · see citing works

Responsible and human centric AI-based insurance advisors

Pisoni, G., & Díaz-Rodríguez, N. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103273 · Cited by 42 · see citing works

Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases

Hasan, M. M., Phu, J., Sowmya, A., Meijering, E. H. W., & Kalloniatis, M. (2023) · Clinical and Experimental Optometry · doi:10.1080/08164622.2023.2235346 · Cited by 42 · see citing works

Survey on ontology-based explainable AI in manufacturing

Naqvi, M. R., Elmhadhbi, L., Sarkar, A., Archimède, B., & Karray, M. H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02304-z · Cited by 42 · see citing works

Multilayer Ensemble Evolving Fuzzy Inference System

Gu, X. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2988846 · Cited by 42 · see citing works

Towards an Explainable Artificial Intelligence approach for smart grid systems

Alfayan, M., & Hagras, H. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00261-5 · Cited by 41 · see citing works

The Unknowability of Autonomous Tools and the Liminal Experience of Their Use

Zhang, Z., Yoo, Y., Lyytinen, K., & Lindberg, A. (2021) · Information Systems Research · doi:10.1287/isre.2021.1022 · Cited by 40 · see citing works

Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making

Giering, S. L. C., Culverhouse, P. F., Johns, D. G., McQuatters‐Gollop, A., & Pitois, S. G. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.986206 · Cited by 39 · see citing works

Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances

Rajbongshi, A., Johora, F. T., Hossain, A., Sarker, M. S., Rahman, M. H., Rahman, M. W., Alotaibi, F. T., & Moni, M. A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11459-5 · Cited by 39 · see citing works

Understandable and trustworthy explainable robots: A sensemaking perspective

Papagni, G., & Koeszegi, S. T. (2020) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2021-0002 · Cited by 39 · 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

Shall AI moderators be made visible? Perception of accountability and trust in moderation systems on social media platforms

Ozanne, M., Bhandari, A., Bazarova, N. N., & DiFranzo, D. J. (2022) · Big Data & Society · doi:10.1177/20539517221115666 · Cited by 37 · see citing works

The Promise and Perils of International Human Rights Law for AI Governance

Su, A. (2022) · Law Technology and Humans · doi:10.5204/lthj.2332 · Cited by 36 · see citing works

Explainable generative design in manufacturing for reinforcement learning based factory layout planning

Klar, M., Ruediger-Flore, P., Schürmann, M., Gören, G. T., Glatt, M. F., Ravani, B., & Aurich, J. C. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.11.012 · Cited by 36 · see citing works

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

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Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

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A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector

Adams, J., & Hagras, H. (2020) · 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177542 · Cited by 35 · see citing works

Explainable Artificial Intelligence: Advancements and Limitations

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Air traffic modernization and control: ADS-B system implementation update 2022: A review

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A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry

Upasane, S. J., Hagras, H., Anisi, M. H., Savill, S., Taylor, I., & Manousakis, K. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3279808 · Cited by 34 · see citing works

An overview of artificial intelligence in the field of genomics

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A multi-genre model for music emotion recognition using linear regressors

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Hybrid intelligence in hospitals: towards a research agenda for collaboration

Mirbabaie, M., Stieglitz, S., & Frick, N. (2021) · Electronic Markets · doi:10.1007/s12525-021-00457-4 · Cited by 34 · see citing works

Human-agent teaming and trust calibration: a theoretical framework, configurable testbed, empirical illustration, and implications for the development of adaptive systems

Bobko, P., Hirshfield, L., Eloy, L., Spencer, C., Doherty, E., Driscoll, J., & Obolsky, H. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2086644 · Cited by 33 · see citing works

The Soul of Work: Evaluation of Job Meaningfulness and Accountability in Human-AI Collaboration

Sadeghian, S., Uhde, A., & Hassenzahl, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637407 · Cited by 33 · see citing works

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

Ouifak, H., & Idri, A. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01610 · Cited by 33 · see citing works

Toward explainable AI-empowered cognitive health assessment

Javed, A. R., Khan, H. U., Alomari, M. K., Sarwar, M. U., Asim, M., Almadhor, A. S., & Khan, M. Z. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1024195 · Cited by 33 · see citing works

Explainable AI and Fuzzy Logic Systems

Chimatapu, R., Hagras, H., Starkey, A., & Owusu, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04070-3_1 · Cited by 32 · see citing works

eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte

Vincenzi, L. (2021) · AMS Laurea Institutional Research Repository (University of Bologna)

Explainable AI (XAI)

Aldahdooh, R., Marouf, A. M., Abu Ghali, M. J., Mahdi, A. O., Abu-Nasser, B. S., & Abu-Naser, S. S. (2025) · PhilPapers (PhilPapers Foundation)

Explainable artificial Advisory and consulting solutions powered by intelligence

Adusupalli, B. (2025) · doi:10.70593/978-93-49910-74-4_10 · Cited by 1 · see citing works

Explainable AI for Non-Expert End Users: A Literature Review

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