Explainable AI: The New 42?

Deep learning

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

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 · see citing works

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 · see citing works

"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

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 · see citing works

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 · see citing works

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 800 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Park, D. H., Hendricks, L. A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., & Rohrbach, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00915 · Cited by 418 · see citing works

Human Activity Recognition Using Recurrent Neural Networks

Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., & Holzinger, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66808-6_18 · Cited by 194 · see citing works

Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital PathologyPreprint

Holzinger, A., Malle, B., Kieseberg, P., Roth, P., Müller, H., Reihs, R., & Zatloukal, K. (2017) · arXiv.org · doi:10.48550/arxiv.1712.06657 · Cited by 113 · see citing works

Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Holzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45507-5_6 · Cited by 98 · 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

Machine Learning Enhanced Virtual Autopsy

Sullivan, S. O., Holzinger, A., Zatloukal, K., Saldiva, P. H. N., Sajid, M. I., & Wichmann, D. (2017) · Autopsy and Case Reports · doi:10.4322/acr.2017.037 · Cited by 30 · see citing works

Working with Beliefs: AI Transparency in the Enterprise.

Chander, A., Srinivasan, R., Chelian, S. E., Wang, J., & Uchino, K. (2018) · IUI Workshops · Cited by 27 · see citing works

Can we Trust Machine Learning Results? Artificial Intelligence in Safety-Critical Decision Support.

Holzinger, K., Mak, K., Kieseberg, P., & Holzinger, A. (2018) · ERCIM news/ERCIM news online edition · Cited by 27 · see citing works

Learning Multi-Way Relations via Tensor Decomposition With Neural Networks

Maruhashi, K., Todoriki, M., Ohwa, T., Goto, K., Hasegawa, Y., Inakoshi, H., & Anai, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11760 · Cited by 24 · see citing works

An Introduction to Deep Visual ExplanationPreprint

Babiker, H. K. B., & Goebel, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09482 · Cited by 15 · see citing works

Evaluating Explanations by Cognitive Value

Chander, A., & Srinivasan, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_23 · Cited by 13 · see citing works

Using KL-divergence to focus Deep Visual ExplanationPreprint

Babiker, H. K. B., & Goebel, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06431 · Cited by 8 · see citing works

Semantic Explanations of PredictionsPreprint

Lécué, F., & Wu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10587 · Cited by 6 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Causability and explainability of artificial intelligence in medicine

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · 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

Fairness of artificial intelligence in healthcare: review and recommendations

Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works

Notions of explainability and evaluation approaches for explainable artificial intelligence

Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works

Counterfactual explanations and how to find them: literature review and benchmarking

Guidotti, R. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00831-6 · Cited by 581 · see citing works

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study

Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01597-4 · Cited by 365 · see citing works

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

Schwalbe, G., & Finzel, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00867-8 · Cited by 381 · see citing works

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Buhrmester, V., Münch, D., & Arens, M. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040048 · Cited by 403 · see citing works

A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022) · Applied Sciences · doi:10.3390/app12031353 · Cited by 339 · see citing works

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Alzubaidi, L., Al‐Amidie, M., Al‐Asadi, A., Humaidi, A. J., Al-Shamma, O., Fadhel, M. A., Zhang, J., Santamaría, J., & Duan, Y. (2021) · Cancers · doi:10.3390/cancers13071590 · Cited by 301 · see citing works

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 · see citing works

Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Holzinger, A., Kieseberg, P., Weippl, E. R., & Tjoa, A. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_1 · Cited by 210 · see citing works

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204 · see citing works

Dermoscopy Image Analysis: Overview and Future Directions

Celebi, M. E., Codella, N., & Halpern, A. C. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2895803 · Cited by 199 · see citing works

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R. (2022) · IEEE Access · doi:10.1109/access.2022.3207765 · Cited by 211 · see citing works

Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions

Longo, L., Goebel, R. G., Lécué, F., Kieseberg, P., & Holzinger, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_1 · Cited by 182 · see citing works

Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

Pisoni, G., Díaz-Rodríguez, N., Gijlers, H., & Tonolli, L. (2021) · Applied Sciences · doi:10.3390/app11020870 · Cited by 183 · see citing works

On the role of knowledge graphs in explainable AI

Lécué, F. (2019) · Semantic Web · doi:10.3233/sw-190374 · Cited by 162 · see citing works

Deep neural network for hierarchical extreme multi-label text classification

Gargiulo, F., Silvestri, S., Ciampi, M., & Pietro, G. D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.041 · Cited by 160 · see citing works

Machine learning and artificial neural network accelerated computational discoveries in materials science

Hong, Y., Hou, B., Jiang, H., & Zhang, J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1450 · Cited by 155 · see citing works

HCI for health and wellbeing: Challenges and opportunities

Blandford, A. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.06.007 · Cited by 152 · see citing works

Understanding artificial intelligence ethics and safetyPreprint

Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 268 · see citing works

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

Piccialli, F., Cola, V. S. D., Giampaolo, F., & Cuomo, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10131-x · Cited by 153 · see citing works

GLocalX—From Local to Global Explanations of Black Box AI Models

Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103457 · Cited by 140 · see citing works

Explainable Artificial Intelligence (XAI) in Biomedicine: Making AI Decisions Trustworthy for Physicians and Patients

Lötsch, J., Kringel, D., & Ultsch, A. (2021) · BioMedInformatics · doi:10.3390/biomedinformatics2010001 · Cited by 116 · see citing works

Digital Transformation for Sustainable Development Goals (SDGs) - A Security, Safety and Privacy Perspective on AI

Holzinger, A., Weippl, E. R., Tjoa, A. M., & Kieseberg, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84060-0_1 · Cited by 115 · see citing works

Explainable artificial intelligence and machine learning: A reality rooted perspective

Emmert‐Streib, F., Yli-Harja, O. P., & Dehmer, M. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1368 · Cited by 106 · see citing works

The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems

Nourani, M., Kabir, S., Mohseni, S., & Ragan, E. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5284 · Cited by 104 · see citing works

Evaluating local explanation methods on ground truth

Guidotti, R. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103428 · Cited by 99 · see citing works

Intelligent software engineering in the context of agile software development: A systematic literature review

Perkusich, M., Silva, L. C. E., Costa, A., Ramos, F., Saraiva, R., Freire, A., Dilorenzo, E., Dantas, E., Santos, D. F. S., Gorgônio, K., et al. (2019) · Information and Software Technology · doi:10.1016/j.infsof.2019.106241 · Cited by 92 · see citing works

Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction

Rakholia, R., Suárez‐Cetrulo, A. L., Singh, M., & Carbajo, R. S. (2024) · IEEE Access · doi:10.1109/access.2024.3458830 · Cited by 103 · see citing works

To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

Amparore, E. G., Perotti, A., & Bajardi, P. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.479 · Cited by 92 · see citing works

Explainable AI and Multi-Modal Causability in Medicine

Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87 · see citing works

Robust Botnet DGA Detection: Blending XAI and OSINT for Cyber Threat Intelligence Sharing

Suryotrisongko, H., Musashi, Y., Tsuneda, A., & Sugitani, K. (2022) · IEEE Access · doi:10.1109/access.2022.3162588 · Cited by 90 · see citing works

Explainable image classification with evidence counterfactual

Vermeire, T., Brughmans, D., Goethals, S., Oliveira, R., & Martens, D. (2022) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01055-y · Cited by 88 · see citing works

Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

Felderer, M., & Ramler, R. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-65854-0_3 · Cited by 84 · see citing works

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira, J. J., & Monteiro, M. D. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49760-6_4 · Cited by 83 · see citing works

AI in learning

Niemi, H. (2021) · Journal of Pacific Rim Psychology · doi:10.1177/18344909211038105 · Cited by 105 · see citing works

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

Al-Tam, R. M., Al-Hejri, A. M., Narangale, S. M., Samee, N. A., Mahmoud, N. F., Al-masni, M. A., & Al-antari, M. A. (2022) · Biomedicines · doi:10.3390/biomedicines10112971 · Cited by 74 · see citing works

Scenario-Based Requirements Elicitation for User-Centric Explainable AI - A Case in Fraud Detection

Cirqueira, D., Nedbal, D., Helfert, M., & Bezbradica, M. (2020) · International Cross-Domain Conference on Machine Learning and Knowledge Extraction · doi:10.1007/978-3-030-57321-8_18 · Cited by 74 · see citing works

Structure‐Based Drug Discovery with Deep Learning**

Özçelik, R., van Tilborg, D., Jiménez-Luna, J., & Grisoni, F. (2023) · ChemBioChem · doi:10.1002/cbic.202200776 · Cited by 69 · see citing works

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Sarp, S., Kuzlu, M., Wilson, E., Cali, Ü., & Güler, Ö. (2021) · Electronics · doi:10.3390/electronics10121406 · Cited by 70 · see citing works

The potential of genomics for infectious disease forecasting

Stockdale, J. E., Liu, P., & Colijn, C. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01233-6 · Cited by 73 · see citing works

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 76 · see citing works

“There Is Not Enough Information”: On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

Schoeffer, J., Kuehl, N., & Machowski, Y. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533218 · Cited by 67 · see citing works

The Use of Responsible Artificial Intelligence Techniques in the Context of Loan Approval Processes

Purificato, E., Lorenzo, F., Fallucchi, F., & Luca, E. W. D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2081284 · Cited by 63 · see citing works

Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis

König, P. D., Wurster, S., & Siewert, M. Β. (2022) · Big Data & Society · doi:10.1177/20539517211069632 · Cited by 65 · see citing works

Catala: a programming language for the law

Merigoux, D., Chataing, N., & Protzenko, J. (2021) · Proceedings of the ACM on Programming Languages · doi:10.1145/3473582 · Cited by 56 · see citing works

Survey of XAI in Digital Pathology

Pocevičiūtė, M., Eilertsen, G., & Lundström, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_4 · Cited by 51 · 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

Understanding citizen perceptions of AI in the smart city

Lehtiö, A., Hartikainen, M., Ala-Luopa, S., Olsson, T., & Väänänen, K. (2022) · AI & Society · doi:10.1007/s00146-022-01589-7 · Cited by 48 · see citing works

Machine Learning Introduces New Perspectives to Data Agency in K—12 Computing Education

Tedre, M., Vartiainen, H., Kahila, J., Toivonen, T., Jormanainen, I., & Valtonen, T. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9274138 · Cited by 48 · see citing works

Detecting and unmasking AI-generated texts through explainable artificial intelligence using stylistic features

Shah, A., Ranka, P., Dedhia, U., Prasad, S., Muni, S., & Bhowmick, K. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01410110 · Cited by 47 · see citing works

Real-time traffic sign detection and classification towards real traffic scene

Wu, Y., Li, Z., Chen, Y., Nai, K., & Yuan, J. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08722-y · Cited by 46 · see citing works

Evaluation Metrics for XAI: A Review, Taxonomy, and Practical Applications

Kadir, M. A., Mosavi, A. H., & Sonntag, D. (2023) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines59282.2023.10297629 · Cited by 52 · see citing works

Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques

Murad, N. Y., Hasan, M. H., Azam, M. H., Yousuf, N., & Yalli, J. S. (2024) · IEEE Access · doi:10.1109/access.2024.3398203 · Cited by 49 · see citing works

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

Campagner, A., Cabitza, F., & Ciucci, D. (2020) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2020.01.010 · Cited by 41 · see citing works

Can machine learning model with static features be fooled: an adversarial machine learning approach

Taheri, R., Javidan, R., Shojafar, M., Vinod, P., & Conti, M. (2020) · Cluster Computing · doi:10.1007/s10586-020-03083-5 · Cited by 37 · see citing works

“Watch out!”: Prediction-Level Intervention for Automated Driving

Wang, C., Krüger, M., & Wiebel-Herboth, C. B. (2020) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3409120.3410652 · Cited by 37 · see citing works

Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

Borrego-Díaz, J., & Galán-Páez, J. (2022) · Minds and Machines · doi:10.1007/s11023-022-09603-z · Cited by 34 · see citing works

Is Artificial Intelligence Ready for Standardization?

Zielke, T. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-56441-4_19 · Cited by 33 · see citing works

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

Veith, B., Krummacker, D., & Schotten, H. D. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3244274 · Cited by 32 · see citing works

Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting

Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., & Cameli, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010017 · Cited by 34 · see citing works

Towards Resilient Artificial Intelligence: Survey and Research Issues

Eigner, O., Eresheim, S., Kieseberg, P., Klausner, L. D., Pirker, M., Priebe, T., Tjoa, S., Marulli, F., & Mercaldo, F. (2021) · Computer Science Symposium in Russia · doi:10.1109/csr51186.2021.9527986 · Cited by 23 · see citing works

From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

Border, S. P., & Sarder, P. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.821217 · Cited by 30 · see citing works

Augmenting Statistical Data Dissemination by Short Quantified Sentences of Natural Language

Hudec, M., Bednárová, E., & Holzinger, A. (2018) · Journal of Official Statistics · doi:10.2478/jos-2018-0048 · Cited by 29 · see citing works

Entropy‐based shadowed set approximation of intuitionistic fuzzy sets

Campagner, A., Dorigatti, V., & Ciucci, D. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22287 · Cited by 29 · see citing works

A review on AI Safety in highly automated driving

Wäschle, M., Thaler, F., Berres, A., Pölzlbauer, F., & Albers, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.952773 · Cited by 28 · see citing works

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Rahim, S. S., Palade, V., Almakky, I., & Holzinger, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_8 · Cited by 26 · see citing works

A Taxonomy of Explainable Bayesian Networks

Derks, I. P., & de Waal, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-66151-9_14 · Cited by 26 · see citing works

Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors

Sivapalan, G., Nundy, K. K., James, A. P., Cardiff, B., & John, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3260722 · Cited by 26 · see citing works

Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach

Öcal, A. (2024) · IEEE Access · doi:10.1109/access.2024.3510526 · Cited by 28 · see citing works

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

Liu, W., Chang, C.-H., Wang, X., Liu, C., Fung, J. M., Ebrahimabadi, M., Karimi, N., Meng, X., & Basu, K. (2021) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2021.3084400 · Cited by 27 · see citing works

Promise of AI in DeFi, a Systematic Review

Sadman, N., Ahsan, M. M., Rahman, A., Siddique, Z., & Gupta, K. D. (2022) · Digital · doi:10.3390/digital2010006 · Cited by 25 · see citing works

Data and model bias in artificial intelligence for healthcare applications in New Zealand

Yogarajan, V., Dobbie, G., Leitch, S. V., Keegan, T. T., Bensemann, J., Witbrock, M., Asrani, V. M., & Reith, D. M. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1070493 · Cited by 24 · see citing works

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

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Explainable AI

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