On generating trustworthy counterfactual explanations
Open accessAbstract
Deep learning models like chatGPT exemplify AI success but necessitate a deeper understanding of trust in critical sectors. Trust can be achieved using counterfactual explanations, which is how humans become familiar with unknown processes; by understanding the hypothetical input circumstances under which the output changes. We argue that the generation of counterfactual explanations requires several aspects of the generated counterfactual instances, not just their counterfactual ability. We present a framework for generating counterfactual explanations that formulate its goal as a multiobjective optimization problem balancing three objectives: plausibility; the intensity of changes; and adversarial power. We use a generative adversarial network to model the distribution of the input, along with a multiobjective counterfactual discovery solver balancing these objectives. We demonstrate the usefulness of six classification tasks with image and 3D data confirming with evidence the existence of a trade-off between the objectives, the consistency of the produced counterfactual explanations with human knowledge, and the capability of the framework to unveil the existence of concept-based biases and misrepresented attributes in the input domain of the audited model. Our pioneering effort shall inspire further work on the generation of plausible counterfactual explanations in real-world scenarios where attribute-/concept-based annotations are available for the domain under analysis.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The study by Del Ser et al. demonstrates across six classification tasks using image and 3D data evidence of trade-offs between objectives and consistency of the generated explanations with human understanding, and the framework's ability to reveal concept-based biases and misrepresented attributes within the model's input domain.
“The study demonstrates across six classification tasks using image and 3D data confirm evidence of trade-offs between objectives and consistency of the generated explanations with human understanding, and the framework's ability to reveal concept-based biases and misrepresented attributes within the model's input domain.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [34]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
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
Self-citationDeep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747 · see citing works
2009 IEEE Conference on Computer Vision and Pattern Recognition
(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,646 · 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
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639 · see citing works
A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10562-9 · Cited by 1,525 · see citing works
Roese, N. J. (1997) · Psychological Bulletin · doi:10.1037/0033-2909.121.1.133 · Cited by 1,156 · see citing works
Counterfactual explanations without opening the black box: automated decisions and the GDPR
Wachter, S., Mittelstadt, B., & Russell, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3063289 · Cited by 1,061 · see citing works
AttGAN: Facial attribute editing by only changing what you want
He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2916751 · Cited by 833 · see citing works
The master algorithm: how the quest for the ultimate learning machine will remake our world
(2016) · Choice Reviews Online · doi:10.5860/choice.194685 · Cited by 808 · see citing works
Deep learning approach for active classification of electrocardiogram signals
Rahhal, M. M. A., Bazi, Y., Alhichri, H. S., Alajlan, N., Melgani, F., & Yager, R. R. (2016) · Information Sciences · doi:10.1016/j.ins.2016.01.082 · Cited by 626 · see citing works
Actionable Recourse in Linear Classification
Ustun, B., Spangher, A., & Liu, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287566 · Cited by 501 · see citing works
AI for life: Trends in artificial intelligence for biotechnology
Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.02.001 · Cited by 438 · see citing works
Self-citationInternational Conference on Computer Vision（ICCV '95）
阿部圭一 (1995) · Cited by 388 · see citing works
Schwalbe, G., & Finzel, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00867-8 · Cited by 381 · see citing works
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works
FACE: Feasible and Actionable Counterfactual Explanations
Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., & Flach, P. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375850 · Cited by 317 · see citing works
Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning
Byrne, R. M. J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/876 · Cited by 285 · see citing works
jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics
Ser, J. D., Benítez‐Hidalgo, A., Nebro, A. J., García-Nieto, J., & Oregi, I. (2019) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.1016/j.swevo.2019.100598 · Cited by 241 · see citing works
Holzinger, A., Saranti, A., Angerschmid, A., Retzlaff, C. O., Gronauer, A., Pejaković, V., Medel-Jiménez, F., Krexner, T., Gollob, C., & Stampfer, K. (2022) · Sensors · doi:10.3390/s22083043 · Cited by 196 · see citing works
Self-citationModel-Agnostic Counterfactual Explanations for Consequential DecisionsPreprint
Karimi, A.-H., Barthe, G., Balle, B., & Valera, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11190 · Cited by 186 · see citing works
2022 ACM Conference on Fairness Accountability and Transparency
(2022) · doi:10.1145/3531146 · Cited by 148 · see citing works
Cognitive neuroscience of human counterfactual reasoning
Hoeck, N. V. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00420 · Cited by 131 · see citing works
On Counterfactual Explanations under Predictive MultiplicityPreprint
Pawelczyk, M., Broelemann, K., & Kasneci, G. (2020) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.2006.13132 · Cited by 106 · see citing works
Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI
Holzinger, A., & Müller, H. (2021) · Computer · doi:10.1109/mc.2021.3092610 · Cited by 95 · see citing works
Self-citationToward human-level concept learning: Pattern benchmarking for AI algorithms
Holzinger, A., Saranti, A., Angerschmid, A., Finzel, B., Schmid, U., & Mueller, H. (2023) · Patterns · doi:10.1016/j.patter.2023.100788 · Cited by 50 · see citing works
Self-citationPredictive Multiplicity in ClassificationPreprint
Marx, C. T., Calmon, F. P., & Ustun, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06677 · Cited by 41 · see citing works
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.
Domingos, P. (2015) · Journal of Computer Science and Technology · Cited by 34 · see citing works
Concept Activation Regions: A Generalized Framework for Concept-Based Explanations
Crabbé, J., & Schaar, M. V. D. (2022) · neural information processing systems · doi:10.52202/068431-0188 · Cited by 27 · see citing works
Saranti, A., Hudec, M., Mináriková, E., Takáč, Z., Großschedl, U., Koch, C., Pfeifer, B., Angerschmid, A., & Holzinger, A. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4040047 · Cited by 25 · see citing works
Self-citationConcept Activation Regions: A Generalized Framework For Concept-Based ExplanationsPreprint
Crabbé, J., & van der Schaar, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.11222 · Cited by 16 · see citing works
The Use and Misuse of Counterfactuals in Ethical Machine Learning
Kasirzadeh, A., & Smart, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445886 · Cited by 11 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Longo, L., Brčić, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 648 · see citing works
Self-citationŞahin, E., Arslan, N. N., & Özdemir, D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10437-2 · Cited by 323 · see citing works
Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212 · see citing works
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
Retzlaff, C. O., Angerschmid, A., Saranti, A., Schneeberger, D. M., Röttger, R., Müller, H., & Holzinger, A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101243 · Cited by 206 · see citing works
Self-citationHuman-Centered AI in Smart Farming: Toward Agriculture 5.0
Holzinger, A., Fister, I., Fister, I., Kaul, H., & Asseng, S. (2024) · IEEE Access · doi:10.1109/access.2024.3395532 · Cited by 213 · see citing works
Self-citationRetzlaff, C. O., Das, S., Wayllace, C., Mousavi, P., Afshari, M. P., Yang, T., Saranti, A., Angerschmid, A., Taylor, M. E., & Holzinger, A. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15348 · Cited by 194 · see citing works
Self-citationOn the failings of Shapley values for explainability
Huang, X., & Marques-Silva, J. P. (2024) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2023.109112 · Cited by 186 · see citing works
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
Transformer models in biomedicine
Madan, S., Lentzen, M., Brandt, J., Rueckert, D., Hofmann‐Apitius, M., & Fröhlich, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02600-5 · Cited by 133 · see citing works
A Practical Tutorial on Explainable AI Techniques
Bennetot, A., Donadello, I., El-Qadi, A., Dragoni, M., Frossard, T., Wagner, B., Saranti, A., Tulli, S., Trocan, M., Chatila, R., et al. (2024) · ACM Computing Surveys · doi:10.1145/3670685 · Cited by 101 · see citing works
Self-citationExplainability pitfalls: beyond dark patterns in explainable AI
Ehsan, U., & Riedl, M. O. (2024) · Patterns · doi:10.1016/j.patter.2024.100971 · Cited by 72 · see citing works
Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
Budhkar, A., Song, Q.-Q., Su, J., & Zhang, X.-H. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.027 · Cited by 71 · see citing works
From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence
Holzinger, A., Schweier, J., Gollob, C., Nothdurft, A., Hasenauer, H., Kirisits, T., Häggström, C., Visser, R. M., Cavalli, R., Spinelli, R., et al. (2024) · Current Forestry Reports · doi:10.1007/s40725-024-00231-7 · Cited by 77 · see citing works
Self-citationHerrera, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103133 · Cited by 61 · see citing works
Self-citationSensors for Digital Transformation in Smart Forestry
Sommer, F., Hönigsberger, F., Gollob, C., Nothdurft, A., Stampfer, K., & Holzinger, A. (2024) · Sensors · doi:10.3390/s24030798 · Cited by 62 · see citing works
Self-citationCLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks
Metsch, J. M., Saranti, A., Angerschmid, A., Pfeifer, B., Klemt, V., Holzinger, A., & Hauschild, A.-C. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104600 · Cited by 53 · see citing works
Self-citationTrustworthy multi-phase liver tumor segmentation via evidence-based uncertainty
Hu, C., Xia, T., Cui, Y., Zou, Q., Wang, Y., Xiao, W., Ju, S., & Li, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108289 · Cited by 53 · see citing works
An interpretable dual attention network for diabetic retinopathy grading: IDANet
Bhati, A., Gour, N., Khanna, P., Ojha, A., & Werghi, N. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102782 · Cited by 45 · see citing works
Grand Challenges of Smart Technology for Older Adults
Zhou, J., Salvendy, G., Boot, W. R., Charness, N. H., Czaja, S. J., Gao, Q., Holzinger, A., Ntoa, S., Rau, P. P., Rogers, W. A., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2457003 · Cited by 50 · see citing works
Self-citationExplainable AI chatbots towards XAI ChatGPT: A review
Kővári, A. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42077 · Cited by 43 · see citing works
Yuan, Z., Li, M., Liu, C., Han, F., Huang, H., & Dai, H. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.02.008 · Cited by 36 · see citing works
Caterson, J., Lewin, A., & Williamson, E. (2024) · Digital Health · doi:10.1177/20552076241272657 · Cited by 40 · see citing works
Nicolson, A., Bradburn, E., Gal, Y., Papageorghiou, A. T., & Noble, J. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02023-0 · Cited by 37 · see citing works
Sablone, S., Bellino, M., Cardinale, A. N., Esposito, M., Sessa, F., & Salerno, M. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1343456 · Cited by 31 · see citing works
Xu, H., Yang, X., Hu, Y., Wang, D., Liang, Z., Mu, H., Wang, Y., Shi, L., Gao, H., Song, D., et al. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2024.100479 · Cited by 31 · see citing works
Abumalloh, R. A., Nilashi, M., Ooi, K., Tan, G. W., & Chan, H. K. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104128 · Cited by 29 · see citing works
Counterfactual explanations for remaining useful life estimation within a Bayesian framework
Andringa, J., Baptista, M. L., & Santos, B. F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102972 · Cited by 26 · see citing works
Torres‐Martos, Á., Anguita‐Ruiz, A., Bustos‐Aibar, M., Ramírez‐Mena, A., Arteaga, M., Bueno, G., Leis, R., Aguilera, C. M., Alcalá, R., & Alcalá‐Fdez, J. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102962 · Cited by 29 · see citing works
Marchesi, R., Micheletti, N., I-Hsien Kuo, N., Barbieri, S., Jurman, G., & Osmani, V. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013080 · Cited by 29 · see citing works
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
Moreira, C., Chou, Y.-L., Hsieh, C., Ouyang, C., Pereira, J. M., & Jorge, J. A. (2024) · ACM Computing Surveys · doi:10.1145/3672553 · Cited by 22 · see citing works
Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning
Pfeifer, B., Gevaert, A., Loecher, M., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121564 · Cited by 22 · see citing works
Self-citationEnhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals
Holzinger, A., Lukač, N., Rozajac, D., Johnston, E., Kocic, V., Hörl, B., Gollob, C., Nothdurft, A., Stampfer, K., Schweng, S., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103032 · Cited by 22 · see citing works
Self-citationKapoor, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127331 · Cited by 22 · see citing works
Neves, J., Hsieh, C., Nobre, I. B., Sousa, S. C., Ouyang, C., Maciel, A., Duchowski, A. T., Jorge, J. A., & Moreira, C. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111341 · Cited by 22 · see citing works
A 2 C: A modular multi-stage collaborative decision framework for human–AI teams
Tariq, S., Chhetri, M. B., Nepal, ., & Paris, C. L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127318 · Cited by 29 · see citing works
IRAF-BRB: An explainable AI framework for enhanced interpretability in project risk assessment
Badhon, B., Chakrabortty, R., Anavatti, S. G., & Vanhoucke, M. (2025) · Expert systems with applications · doi:10.1016/j.eswa.2025.127979 · Cited by 20 · see citing works
Joshi, R. C., Rai, J. K., Bürget, R., & Dutta, M. K. (2024) · ISA Transactions · doi:10.1016/j.isatra.2024.12.008 · Cited by 20 · see citing works
Fang, X., Zhang, J., Zhao, X., Zhang, L., Zhou, D., Yu, C., Hu, W., & Zhang, Q. (2024) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2024.06.001 · Cited by 18 · see citing works
The revolution and vision of explainable AI for Android malware detection and protection
Ullah, S., Li, J., Ullah, F., Chen, J., Ali, I., Khan, S., Ahad, A., & Leung, V. C. M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101320 · Cited by 17 · see citing works
Food recommendation towards personalized wellbeing
Qiao, G., Zhang, D., Zhang, N., Shen, X., Jiao, X., Lu, W., Fan, D., Zhao, J., Zhang, H., Chen, W., et al. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104877 · Cited by 19 · see citing works
Kudelić, R., Šmaguc, T., & Robinson, S. (2025) · Financial Innovation · doi:10.1186/s40854-025-00759-y · Cited by 21 · see citing works
Explaining and visualizing black-box models through counterfactual paths
Pfeifer, B., Krzyziński, M., Baniecki, H., Holzinger, A., & Biecek, P. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01532-8 · Cited by 15 · see citing works
Self-citationPortraying on-road CO2 concentrations using street view panoramas and ensemble learning
Zhang, Y., Sun, T.-L., Wang, L., Huang, B., Pan, X., Song, W., Wang, K., Xiong, X., Xu, S., Yao, L., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174326 · Cited by 15 · see citing works
Usability in human-robot collaborative workspaces
Schraick, L.-M., Sommer, F., Stampfer, K., Meixner, O., & Holzinger, A. (2024) · Universal Access in the Information Society · doi:10.1007/s10209-024-01163-6 · Cited by 15 · see citing works
Self-citationSuffian, M., Kuhl, U., Bogliolo, A., & Alonso-Moral, J. M. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103484 · Cited by 14 · see citing works
Customer-Centric Decision-Making with XAI and Counterfactual Explanations for Churn Mitigation
Oprea, S., & Bârã, A. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20020129 · Cited by 13 · see citing works
Complexity data science: A spin-off from digital twins
Emmert‐Streib, F., Cherifi, H., Kaski, K. K., Kauffman, S. A., & Yli-Harja, O. P. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae456 · Cited by 15 · see citing works
Hönigsberger, F., Saranti, A., Jalali, A., Stampfer, K., & Holzinger, A. (2024) · Applied Sciences · doi:10.3390/app14093911 · Cited by 12 · see citing works
Self-citationA quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals
Oh, K., Heo, D.-W., Mulyadi, A. W., Jung, W., Kang, E., Lee, K. H., & Suk, H. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121077 · Cited by 12 · see citing works
Developing user-centered system design guidelines for explainable AI: a systematic literature review
Hong, S., & Park, W. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11363-y · Cited by 11 · see citing works
Collaborative artificial intelligence system for investigation of healthcare claims compliance
Sbodio, M. L., López, V., Hoang, T. L., Brisimi, T. S., Picco, G., Vejsbjerg, I., Rho, V., Aonghusa, P. M., Kristiansen, M., & Segrave-Daly, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-62665-0 · Cited by 12 · see citing works
Xuan, Y., Small, E. A., Sokol, K., Hettiachchi, D., & Sanderson, M. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103376 · Cited by 12 · see citing works
Adarsh, V., & Gangadharan, G. R. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110318 · Cited by 11 · see citing works
From scientific theory to duality of predictive artificial intelligence models
Bajorath, J. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102516 · Cited by 11 · see citing works
Sanderson, J., Mao, H., & Woo, W. L. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3552057 · Cited by 10 · see citing works
ZFort: A scalable zero-trust approach for trust management and traffic engineering in SDN based IoTs
Ashraf, U., Al-Naeem, M., Bhutta, M. N. M., & Yuen, C. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101419 · Cited by 10 · see citing works
On the disagreement problem in Human-in-the-Loop federated machine learning
Huelser, M. J. M., Mueller, H., Díaz-Rodríguez, N., & Holzinger, A. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100827 · Cited by 12 · see citing works
Self-citationHein, I., & Diefenbach, S. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2502983 · Cited by 10 · see citing works
Explaining 3D Semantic Segmentation Through Generative AI ‐Based Counterfactuals
Rozajac, D., Lukač, N., Schweng, S., Gollob, C., Nothdurft, A., Stampfer, K., Ser, J. D., & Holzinger, A. (2025) · Expert Systems · doi:10.1111/exsy.70163 · Cited by 10 · see citing works
Self-citationTABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE
Panagiotou, E., Heurich, M., Landgraf, T., & Ntoutsi, E. (2024) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3677052.3698673 · Cited by 9 · see citing works
Kočić, V., Lukač, N., Rozajac, D., Schweng, S., Gollob, C., Nothdurft, A., Stampfer, K., Ser, J. D., & Holzinger, A. (2025) · IEEE Access · doi:10.1109/access.2025.3568052 · Cited by 9 · see citing works
Self-citationZhu, A. Y. F. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102599 · Cited by 12 · see citing works
Núñez‐Delgado, A. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173310 · Cited by 8 · see citing works
Majority voting of doctors improves appropriateness of AI reliance in pathology
Gu, H., Yang, C., Magaki, S., Zarrin‐Khameh, N., Lakis, N. S., Cobos, I., Khanlou, N., Zhang, X. R., Assi, J. S., Byers, J. T., et al. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103315 · Cited by 6 · see citing works
Gentile, D., Donmez, B., & Jamieson, G. A. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103434 · Cited by 7 · see citing works
Mohanty, P. K., Francis, S. A. J., Barik, R. K., Reddy, K. H. K., Roy, D. S., & Saikia, M. J. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2839 · Cited by 7 · see citing works
Diverse Counterfactual Explanations (DiCE) Role in Improving Sales and e-Commerce Strategies
Oprea, S., & Bârã, A. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20020096 · Cited by 7 · see citing works
Sahoh, B., Kliangkhlao, M., Haruehansapong, K., Yeranee, K., & Punsawad, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3432766 · Cited by 7 · see citing works
Dandl, S., Blesch, K., Freiesleben, T., König, G., Kapar, J., Bischl, B., & Wright, M. N. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_5 · Cited by 6 · see citing works
Agent-in-the-loop to distill expert knowledge into artificial intelligence models: a survey
Gao, J., Zhang, Y., Chen, Y., Dong, Y., Chen, Y., Song, S., Tang, B., & Gu, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11255-1 · Cited by 7 · see citing works
Kuhl, U., & Bush, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08333-3_12 · Cited by 6 · see citing works
Advancing resilience in infrastructure projects through machine learning-driven models
Ojiako, U., Wong, T., Smith, C. J., Chipulu, M., Al-mhdawi, M. K., Oyewo, B., & Obokoh, L. (2025) · Production Planning & Control · doi:10.1080/09537287.2025.2583300 · Cited by 7 · see citing works
Musacchio, N., Zilich, R., Masi, D., Baccetti, F., Nreu, B., Giorda, C. B., Guaita, G., Morviducci, L., Marco Muselli, B., Ozzello, A., et al. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010021 · Cited by 6 · see citing works
OCIE: Augmenting model interpretability via Deconfounded Explanation-Guided Learning
Dong, L., Chen, L., Zheng, C., Fu, Z., Zukaib, U., Cui, X., & Shen, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112390 · Cited by 6 · see citing works
Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for CybersecurityPreprint
Tariq, S., Singh, R., Chhetri, M. B., Nepal, ., & Paris, C. L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5462737 · Cited by 6 · see citing works
Counterfactual Explanations in Education: A Systematic Review
Guisñan, P. A. B., Lara, J. A., & Romero, C. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70060 · Cited by 5 · see citing works
AI on the path to good decisions
Kiškis, M. (2026) · Sustainable Futures · doi:10.1016/j.sftr.2026.101644 · Cited by 5 · see citing works
Naumova, K., Devos, A., Karimireddy, S. P., Jaggi, M., & Hartley, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01226-1 · Cited by 5 · see citing works
Kibria, H. B., Hossain, M. A., Rehman, S., Alahakoon, D., & Rahman, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10854-3 · Cited by 5 · see citing works
Rong, Y., Lin, T., Chen, H., Fan, Z., & Chen, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3477932 · Cited by 5 · see citing works
Zuo, L., & Mak, M. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3566767 · Cited by 4 · see citing works
CIRF: Importance of related features for plausible counterfactual explanations
Kim, H.-D., Ju, Y.-J., Hong, J.-H., & Lee, S. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120974 · Cited by 4 · see citing works
Rosati, R., Romeo, L., & Mancini, A. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110763 · Cited by 4 · see citing works
Zeng, D., Legaspi, R., Sun, Y., Dong, X., Ikeda, K., Spirtes, P., & Zhang, K. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3728318 · Cited by 4 · see citing works
Pascual-Triana, J. D., Fernández, A., Ser, J. D., & Herrera, F. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121844 · Cited by 3 · see citing works
Self-citationImproving Crowdfunding Decisions Using Explainable Artificial Intelligence
Gregoriades, A., & Themistocleous, C. (2025) · Sustainability · doi:10.3390/su17041361 · Cited by 3 · see citing works
A detailed analysis into evaluation metrics for knowledge graph evaluation
Hassan, H., Elayidom, M. S., Irshad, M. R., & Chesneau, C. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00871-3 · Cited by 3 · see citing works
Mohammadi, A. R., Peintner, A., Müller, M., & Zangerle, E. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748028 · Cited by 3 · see citing works
Cuzzocrea, A., Folino, F., Pontieri, L., Sabatino, P., & Samami, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11360-w · Cited by 3 · see citing works
TrustML: A Python package for computing the trustworthiness of ML models
Manzano, M., Ayala, C. P., & Gómez, C. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101740 · Cited by 3 · see citing works
Bridging the divide: technical research and application on legal judgment prediction
Zhang, C., & Meng, Y. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09473-7 · Cited by 7 · see citing works
On the next generation for neuromorphic computing and neuromorphic AI
Grindrod, P. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2025.0122 · Cited by 2 · see citing works
COCOA: Cost-Optimized COunterfactuAl explanation method
Mediavilla-Relaño, J., & Lázaro, M. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120616 · Cited by 2 · see citing works
Bhowmick, S. K., Barman, A., & Roy, S. K. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105068 · Cited by 2 · see citing works
Multi-agent reinforcement learning system framework based on topological networks in Fourier space
Sun, L., Ding, A., & Ma, H. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112986 · Cited by 2 · see citing works
Integrating Explainable AI with Machine Learning for Accurate Liver Disease Diagnosis
Konyala, A. R., Jo, A. A., & Raj, E. D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1918-4_26 · Cited by 2 · see citing works
Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models
Xu, A., Li, Z., Zhang, Y., & Wu, T. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3554511 · Cited by 2 · see citing works
Yu, N., chen, L., Yi, X., & Huang, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105215 · Cited by 1 · see citing works
Gao, L., Yuan, X., Shen, X., & Sun, J. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114777 · Cited by 1 · see citing works
Explainability as a Structural Property: An Empirical Analysis of Rashomon Sets and Pareto Fronts
Solano, R. P., Berlanga, A., Molina, J. M., & Julio, Y. R. (2026) · Algorithms · doi:10.3390/a19050358 · Cited by 1 · see citing works
- Authors
- Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A.
- Affiliations
- Universidad de Granada (ES)
- Medical University of Graz (AT)
- Corresponding author
- Holzinger, Andreas
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 655
- Pages
- 119898
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4388767362
- Cited by
- 122
- References
- 39
- Citations by year
- 2026: 22 · 2025: 49 · 2024: 51
- FWCI
- 11.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Sciences (publishedVersion, cc-by)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Funders
- European Commission (CER-20211030)
- Horizon 2020 (826078)
- Austrian Science Fund (P-32554)
- H2020 Marie Skłodowska-Curie Actions (101059332)
- Centro para el Desarrollo Tecnológico Industrial
- Eusko Jaurlaritza (IT1256-22)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- counterfactual explanations, trustworthy AI, multi-objective optimization, generative adversarial networks, plausibility, explainable deep learning, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
