Explainable AI: The New 42?

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387

"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

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

Moravčík, M., Schmid, M., BURCH, N. R., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017) · Science · doi:10.1126/science.aam6960 · Cited by 885

What do we need to build explainable AI systems for the medical domain?

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534

Epistemic Insights as Design Principles for a Teaching-Learning Module on Artificial Intelligence

Barelli, E., Lodi, M., Branchetti, L., & Levrini, O. (2024) · Science & Education · doi:10.1007/s11191-024-00504-4 · Cited by 25

Explainable AI: The Quest for Transparency in Business and Beyond

DeSimone, H., & León, M. (2024) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict62343.2024.00093 · Cited by 20

Recent Trends in XAI: A Broad Overview on current Approaches, Methodologies and Interactions.

Schoenborn, J. M., & Althoff, K. (2019) · ICCBR Workshops · Cited by 16

Securing industry 4.0: a systematic review of AI-driven intrusion detection approaches and emerging trends

Kalpani, N., & Rodrigo, N. (2025) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-025-00264-0 · Cited by 16

Artificial stupidity

Falk, M. (2021) · Interdisciplinary Science Reviews · doi:10.1080/03080188.2020.1840219 · Cited by 15

Moody Learners - Explaining Competitive Behaviour of Reinforcement Learning Agents

Barros, P. V. A., Tanevska, A., Cruz, F., & Sciutti, A. (2020) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl-epirob48136.2020.9278125 · Cited by 13

Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

Aslam, N. (2022) · Computation · doi:10.3390/computation10030036 · Cited by 15

The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionsPreprint

Ferreira, J. J., & Monteiro, M. D. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05460 · Cited by 15

Logic programming for deliberative robotic task planning

Meli, D., Nakawala, H., & Fiorini, P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10389-w · Cited by 18

COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Ikechukwu, A. V., Murali, S., & Honnaraju, B. (2023) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon58499.2023.10270604 · Cited by 16

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Baron, B., & Musolesi, M. (2017) · IEEE pervasive computing · doi:10.1109/mprv.2019.2918540 · Cited by 14

A Study on Fairness and Trust Perceptions in Automated Decision MakingPreprint

Schoeffer, J., Machowski, Y., & Kuehl, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04757 · Cited by 31

Artistic Style Meets Artificial Intelligence

Choi, S. K., DiPaola, S., & Töyrylä, H. (2021) · Journal of Perceptual Imaging · doi:10.2352/j.percept.imaging.2021.4.2.020501 · Cited by 13

Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools

Kousa, P., & Niemi, H. (2022) · Springer eBooks · doi:10.1007/978-3-031-09687-7_17 · Cited by 13

Bridging the gap between technological advance and professional psychology training: A way forward.

Hagstrom, S. L., & Maranzan, K. A. (2019) · Canadian Psychology/Psychologie canadienne · doi:10.1037/cap0000186 · Cited by 15

A Review of Machine Learning and Cryptography Applications

Sooksatra, K., & Rivas, P. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00105 · Cited by 14

Explaining the impact of source behaviour in evidential reasoning

Kowalski, P., Zocholl, M., & Jousselme, A.-L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.007 · Cited by 13

Machine learning control — explainable and analyzable methods

Quade, M., Isele, T., & Abel, M. (2020) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2020.132582 · Cited by 17

From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata

Schwammberger, M., & Klös, V. (2022) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.371.2 · Cited by 12

Neural Network-Based Method for Early Diagnosis of Autism Spectral Disorder Head-Banging Behavior from Recorded Videos

Sadek, E. T., Seada, N. A., & Ghoniemy, S. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423560037 · Cited by 12

Exploration of Explainable AI in Context of Human-Machine Interface for the Assistive Driving System

Chaczko, Z., Kulbacki, M., Gudzbeler, G., Alsawwaf, M., Thai-Chyzhykau, I., & Wajs-Chaczko, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-42058-1_42 · Cited by 11

Digitalization in psychology: A bit of challenge and a byte of success

Ostermann, T., Röer, J. P., & Tomasik, M. J. (2021) · Patterns · doi:10.1016/j.patter.2021.100334 · Cited by 16

Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity RecognitionPreprint

Nourani, M., Roy, C., Rahman, T., Ragan, E. D., Ruozzi, N., & Gogate, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02335 · Cited by 24

Explainable AI (XAI): History, Basic Ideas and Methods

Zodage, P., Harianawala, H., Shaikh, H., & Kharodia, A. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-16988 · Cited by 14

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Ikechukwu, A. V. (2024) · INTELIGENCIA ARTIFICIAL · doi:10.4114/intartif.vol27iss74pp133-151 · Cited by 12

What’s on Your Mind, NICO?

Kerzel, M., Ambsdorf, J., Becker, D., Lu, W., Strahl, E., Spisak, J., Gäde, C., Weber, T., & Wermter, S. (2022) · Künstliche Intell. · doi:10.1007/s13218-022-00772-8 · Cited by 11

XAINES: Explaining AI with Narratives

Hartmann, M., Du, H., Feldhus, N., Kruijff‐Korbayová, I., & Sonntag, D. (2022) · Künstliche Intell. · doi:10.1007/s13218-022-00780-8 · Cited by 13

Explainable Detection of Zero Day Web Attacks

Sejr, J. H., Zimek, A., & Schneider–Kamp, P. (2020) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis50059.2020.00016 · Cited by 10

AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations

Cao, B., Greiner, R., Greenshaw, A. J., & Sui, J. (2025) · Journal of Medical Internet Research · doi:10.2196/66100 · Cited by 12

Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks

Drosos, I., Williams, J., Sarkar, A., Wilson, N., Rintel, S., & Panda, P. (2025) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3729176.3729203 · Cited by 17

On-the-fly simplification of genetic programming models

Javed, N., & Gobet, F. R. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3441926 · Cited by 9

Subtyping patients with chronic disease using longitudinal BMI patterns

Mottalib, M. M., Jones‐Smith, J. C., Sheridan, B., & Beheshti, R. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3237753 · Cited by 13

The axiomatization of asymmetric disjunction and conjunction

Hudec, M., & Mesiar, R. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.06.018 · Cited by 9

The AI for Scientific Discovery Network+

Kanza, S., Bird, C., Niranjan, M., McNeill, W., & Frey, J. G. (2021) · Patterns · doi:10.1016/j.patter.2020.100162 · Cited by 9

Toward reliable machine learning with Congruity: a quality measure based on formal concept analysis

Maio, C. D., Fenza, G., Gallo, M., Loia, V., & Stanzione, C. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07853-7 · Cited by 9

Selection of powerful radio galaxies with machine learning

Carvajal, R., Matute, I., Afonso, J., Norris, R. P., Luken, K., Sanchéz-Sáez, P., Cunha, P. A. C., Humphrey, A., Messias, H., Amarantidis, S., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245770 · Cited by 8

The promise of machine learning in violent conflict forecasting

Murphy, M., Sharpe, E., & Huang, K. (2024) · Data & Policy · doi:10.1017/dap.2024.27 · Cited by 8

An End-to-End Trainable Deep Convolutional Neuro-Fuzzy Classifier

Yeganejou, M., Kluzinski, R., Dick, S., & Miller, J. (2022) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee55066.2022.9882723 · Cited by 9

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine

Gniadek, T. J., Kang, J., Theparee, T., & Krive, J. (2023) · Online Journal of Public Health Informatics · doi:10.2196/50934 · Cited by 8

Engineering Responsible And Explainable Models In Human-Agent Collectives

Abeywickrama, D. B., & Ramchurn, S. D. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2282834 · Cited by 8

Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debugging

Bravo-Rocca, G., Liu, P., Guitart, J., Dholakia, A., Ellison, D. D., Falkanger, J., & Hodak, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117232 · Cited by 9

Revisiting Habitability in Conversational Systems

Kocaballi, A. B., Coiera, E. W., & Berkovsky, S. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3383014 · Cited by 8

The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate-Measures Using Machine Learning

Hillege, R. H. L., Lo, J. C., Janssen, C. P., & Romeijn, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50788-6_24 · Cited by 8

Transparency of Classification Systems for Clinical Decision Support

Richard, A., Mayag, B., Talbot, F., Tsoukiàs, A., & Meinard, Y. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-50153-2_8 · Cited by 9

Expectations of Artificial Intelligence for Pathology

Regitnig, P., Müller, H., & Holzinger, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_1 · Cited by 8

Multilevel Explainable Artificial Intelligence: Visual and Linguistic Bonded Explanations

Aysel, H. I., Cai, X., & Prügel‐Bennett, A. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3308555 · Cited by 8

Unveiling sentiment analysis: A comparative study of LSTM and Logistic regression models with XAI insights

Chandu, D. V., Mayuri, B., Yash, R., Sagar, K., Soham, L., Soham, P., & Shreyash, B. (2023) · i-manager s Journal on Computer Science · doi:10.26634/jcom.11.3.20471 · Cited by 7

In Search of Trustworthy and Transparent Intelligent Systems With Human-Like Cognitive and Reasoning Capabilities

Pal, N. R. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00076 · Cited by 9

A predictive and user-centric approach to Machine Learning in data streaming scenarios

Carneiro, D. R., Guimarães, M., Silva, F., & Nováis, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.100 · Cited by 7

Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction

Hsu, W. W. Y., Warren, J., & Riddle, P. (2022) · Methods of Information in Medicine · doi:10.1055/s-0042-1758687 · Cited by 7

Dialogical Guidelines Aided by Knowledge Acquisition: Enhancing the Design of Explainable Interfaces and Algorithmic Accuracy

Gerdes, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63128-4_19 · Cited by 7

Using deep learning to identify translational research in genomic medicine beyond bench to bedside

Hsu, Y.-Y., Clyne, M., Wei, C.-H., Khoury, M. J., & Lu, Z. (2019) · Database · doi:10.1093/database/baz010 · Cited by 6

Current trends of artificial intelligence and applications in digital pathology: A comprehensive review

Goswami, N. G., Karnad, S., Sampathila, N., Bairy, G. M., Chadaga, K., & Swathi, K. S. (2023) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2023.12.004 · Cited by 6

Social Media Analytics in Operations and Supply Chain Management: Opportunities, Challenges, and Paradoxes

Kinra, A., Siekmann, F., & Kotzab, H. W. (2022) · Advanced studies in diginomics and digitalization · doi:10.1007/978-3-031-04063-4_10 · Cited by 6

Opportunities for human factors in machine learning

Baweja, J. A., Fallon, C. K., & Jefferson, B. A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1130190 · Cited by 5

Learning to Control a Quadcopter Qualitatively

Šoberl, D., Ivan Bratko, A., & Žabkar, J. (2020) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-020-01228-7 · Cited by 6

Considerations for Comparing Video Game AI Agents with Humans

Madan, C. R. (2020) · Challenges · doi:10.3390/challe11020018 · Cited by 7

Quantifying the Demand for Explainability

Weber, T., Hußmann, H., & Eiband, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_38 · Cited by 7

There is no Artificial General IntelligencePreprint

Landgrebe, J., & Smith, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05833 · Cited by 21

Targeting resources efficiently and justifiably by combining causal machine learning and theory

Ali, Ö. G. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1015604 · Cited by 7

Mitigative Strategies for Recovering From Large Language Model Trust Violations

Martell, M. J., Baweja, J. A., & Dreslin, B. D. (2024) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434241303577 · Cited by 8

The Role of Big Data and Artificial Intelligence in Clinical Research and Digital Therapeutics

Kyriazakos, S. A., Pnevmatikakis, A., den Akker, H. O., & Kostopoulou, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-32614-1_6 · Cited by 5

MxML (Exploring the Relationship between Measurement and Machine Learning): Current State of the Field

Zheng, Y., Nydick, S. W., Huang, S.-J., & Zhang, S. (2024) · Educational Measurement: Issues and Practice · doi:10.1111/emip.12593 · Cited by 4

The Practical Epistemologies of Design and Artificial Intelligence

Billingsley, W. H. (2024) · Science & Education · doi:10.1007/s11191-024-00517-z · Cited by 11

Deep Learning Visualization for Underspecification Analysis in Product Design Matching Model Development

Chotisarn, N., Pimanmassuriya, W., & Gulyanon, S. (2021) · IEEE Access · doi:10.1109/access.2021.3102174 · Cited by 5

Impact of Machine Learning on Safety Monitors

Terrosi, F., Strigini, L., & Bondavalli, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14835-4_9 · Cited by 5

Explainability and Interpretability Concepts for Edge AI Systems

Vermesan, O., Piuri, V., Scotti, F., Genovese, A., Labati, R. D., & Coscia, P. (2024) · River Publishers eBooks · doi:10.1201/9781003478713-9 · Cited by 5

Cognitive Production Systems: A Mapping Study

Ghofrani, J., Deutschmann, B., Soorati, M. D., Reichelt, D., & Ihlenfeldt, S. (2020) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45582.2020.9442230 · Cited by 5

Explaining the Unexplainable: A Systematic Review of Explainable AI in FinancePreprint

Mohsin, M. T., & Nasim, N. B. (2025) · Qeios · doi:10.32388/puo5ck · Cited by 7

Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures

Campagner, A., Milella, F., Banfi, G., & Cabitza, F. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02602-3 · Cited by 4

Towards Explainable Direct Marketing in the Telecom Industry Through Hybrid Machine Learning

Petersen, R. M., & Daramola, O. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58817-5_35 · Cited by 4

Application of Interpretable Artificial Intelligence Enabled Cognitive Internet of Things for COVID-19 Pandemics

Awotunde, J. B., Jimoh, R. G., Adeniyi, A. E., Ayo, F. E., Ajamu, G. J., & Aremu, D. R. (2023) · Springer eBooks · doi:10.1007/978-3-031-38036-5_11 · Cited by 5

Distance-Aware eXplanation Based Learning

Hagos, M. T., Belton, N., Curran, K. M., & Namee, B. M. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00048 · Cited by 5

Aggregation of Fuzzy Conformances

Hudec, M., & Vučetić, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19494-9_28 · Cited by 4

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Gutiérrez, D. Á., Lasheras, F. S., Martín, V., Gómez, S. L. S., Moreno, V. R., Moratalla-Navarro, F., & Molina, A. J. (2022) · Mathematics · doi:10.3390/math10071024 · Cited by 4

Automatic Sorting of Dwarf Minke Whale Underwater Images

Konovalov, D. A., Swinhoe, N., Efremova, D. B., Birtles, A., Kusetic, M., Hillcoat, S., Curnock, M., Williams, G., & Sheaves, M. J. (2020) · Information · doi:10.3390/info11040200 · Cited by 5

Learning Optimal Decision Trees from Large DatasetsPreprint

Avellaneda, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06314 · Cited by 5

Explainable Machine Learning Control -- robust control and stability analysisPreprint

Quade, M., Isele, T., & Abel, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10056 · Cited by 4

On Explainability in AI-Solutions: A Cross-Domain Survey

Antón, S. D. D., Schneider, D., & Schotten, H. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14862-0_17 · Cited by 4

Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components

Bossuyt, D. L. V., Papakonstantinou, N., Hale, B., Salonen, J., & O’Halloran, B. M. (2022) · Artificial Intelligence and Cybersecurity · doi:10.1007/978-3-031-15030-2_3 · Cited by 3

Efficient Shapely Explanation of Support Vector Regression for Agile and Non-agile Software Effort Estimation

Najm, A., Zakrani, A., & Marzak, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7663-6_67 · Cited by 3

SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

Xing, N., Yeung, S. H., Cai, C., Ng, T. K., Wang, W., Yang, K., Yang, N., Zhang, M., Chen, G., & Ooi, B. C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475176 · Cited by 3

Emergence of norms in interactions with complex rewards

Abeywickrama, D. B., Griffiths, N., Xu, Z., & Mouzakitis, A. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09585-3 · Cited by 3

Fact-checking with explanations

Groza, A. P., & Katona, Á. (2022) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc57785.2022.00031 · Cited by 3

Conceptual Legal Challenges in Tax Automation via Blockchain Technology

Dimitropoulou, C., Lazarov, I., & Lawson, Y. (2023) · World Tax Journal · doi:10.59403/1qmggaj · Cited by 4

Legal and Technical Answers to Privacy Issues raised by AI-based Facial Recognition Algorithms

Capasso, C., Zingoni, A., Calabrò, G., & Sterpa, A. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405669 · Cited by 3

Explainability in threat assessment with evidential networks and sensitivity spaces

Kowalski, P., Zocholl, M., & Jousselme, A.-L. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190475 · Cited by 3

Human in Focus: Future Research and Applications of Ubiquitous User Monitoring

Lau, N. K. C., Hildebrandt, M., Althoff, T., Boyle, L. N., Iqbal, S. T., Lee, J. D., Mehta, R. K., & Poore, J. (2019) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181319631317 · Cited by 3

Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model

Wolff, J. G. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-64949-4_7 · Cited by 3

Artificial Intelligence in Cardiovascular Medicine: A Comprehensive Clinical ReviewPreprint

Ganipineni, V. D. P., Gutlapalli, S. D., Paramsothy, J., Okorie, I. J., Ugwendum, D. F., Farid, M., Fabián, D., Kailayanathan, T., Piccione, G., Idavalapati, A. S. K. K., et al. (2023) · Preprints.org · doi:10.20944/preprints202305.1061.v1 · Cited by 3

Explaining the Unexplainable: A Systematic Review of Explainable AI in FinancePreprint

Mohsin, M. T., & Nasim, N. B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5436998 · Cited by 3

XAI Approach to Improved and Informed Detection of Burnt Scar

Maneerat, T. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765051 · Cited by 2

A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization

Akbarzadeh, S., Ahderom, S., & Alameh, K. (2019) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.191219.001 · Cited by 2

Supplementing Machine Learning with Knowledge Models Towards Semantic Explainable AI

Sander, J., & Kuwertz, A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-74009-2_1 · Cited by 2

User Involvement in the Design of ML-Infused Systems

Karahasanović, A., Nilsson, E. G., Grani, G., Pettersen, K. F., Karabeg, L., & Schittekat, P. (2021) · International Conference of the ACM Greek SIGCHI Chapter (CHIGreece) · doi:10.1145/3489410.3489421 · Cited by 2

FAIR AI: A Conceptual Framework for Democratisation of 21st Century AI

Halgamuge, S. K. (2021) · International Conference on Instrumentation, Control, and Automation (ICA) · doi:10.1109/ica52848.2021.9625672 · Cited by 2

Inclusive Privacy Control at Home for Smart Health

Manohar, A. K., Sengul, C., & Chen, J. (2023) · Postdigital science and education · doi:10.1007/978-3-031-31875-7_9 · Cited by 2

Advancing Edge Artificial Intelligence

Vermesan, O., & Marples, D. (2024) · River Publishers eBooks · doi:10.1201/9781003478713 · Cited by 2

Machine Learning Classifiers Explanations with Prototype Counterfactual

Kumar, A., Dwivedi, S., Mehta, A., & Malhotra, V. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-47221-3_15 · Cited by 2

HOPE: High-Order Polynomial Expansion of Black-Box Neural Networks

Xiao, T., Zhang, W., Cheng, Y., & Suo, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3399197 · Cited by 4

Explainable AI (ex-AI)

Holzinger, A. (2018) · Informatik-Spektrum · doi:10.1007/s00287-018-1102-5 · Cited by 67

From Machine Learning to Explainable AI

Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306

Scope and Sense of Explainability for AI-Systems

Leventi-Peetz, A.-M., Östreich, T., Lennartz, W., & Weber, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82193-7_19 · Cited by 2

Explainable Artificial Intelligence: A Narrative Review from Rule-Based System Explanations to Post-hoc Attribution and Interpretable Models

Revista, Z., & IA, 1. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22172812

Explainable AI

Dix, A. J., & Finlay, J. (2025) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003082880-25