Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707

"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

Untitled work

(1988) · Cited by 16,582

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

Causality : Models , Reasoning , and Inference

Bentler, P., Heslenfeld, D., Molenaar, P., & Mcdonald, R. (2002) · Choice Reviews Online · doi:10.5860/choice.47-3771 · Cited by 13,039

Causality: Models, Reasoning, and Inference

Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653

A Value for n-person Games

Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561

From local explanations to global understanding with explainable AI for trees

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551

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

Explainable person–job recommendations: challenges, approaches, and comparative analysis

Tang, F., Zhu, R., Yao, F., Wang, J., Luo, L., & Li, B. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1660548 · Cited by 17

AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing

Narra, M., Ray, A., Polley, B., Yang, H., & Bhowmik, P. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1718810 · Cited by 16

Opacity, Machine Learning and Explainable AI

Fernández, A. (2023) · The International library of ethics, law and technology · doi:10.1007/978-3-031-48135-2_3 · Cited by 13

TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS

Grespan, M., Thuruthipilly, H., Pollo, A., Lochner, M., Biesiada, M., & Etsebeth, V. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202449929 · Cited by 16

Tertiary Review on Explainable Artificial Intelligence: Where Do We Stand?

van Mourik, F., Jutte, A., Berendse, S. E., Bukhsh, F. A., & Ahmed, F. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6030098 · Cited by 13

AI Ethics’ Institutional Turn

Maclure, J., & Morin-Martel, A. (2025) · Digital Society · doi:10.1007/s44206-025-00174-x · Cited by 13

Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation

Yang, Q., Ma, C., Zhang, Q., Gao, X., Zhang, C., & Zhang, X. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599289 · Cited by 13

Human-Centric explanations for users in automated Vehicles: A systematic review

Zhu, Z., Li, X., Delhomme, P., Schroeter, R., Glaser, S., & Rakotonirainy, A. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108152 · Cited by 12

Characteristics of Fatal Occupational Injuries in Migrant Workers in South Korea: A Machine Learning Study

Lee, J.-Y., Lee, W., & Cho, S. (2022) · Heliyon · doi:10.1016/j.heliyon.2023.e20138 · Cited by 13

Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data

Jung, G., Park, S., Ma, E.-Y., Kim, H., & Lee, U. (2024) · ACM Computing Surveys · doi:10.1145/3648356 · Cited by 11

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations

Artelt, A., & Gregoriades, A. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114249 · Cited by 14

Unveiling the decision making process in Alzheimer’s disease diagnosis: A case-based counterfactual methodology for explainable deep learning

Adarsh, V., & Gangadharan, G. R. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110318 · Cited by 15

Developing guidelines for functionally-grounded evaluation of explainable artificial intelligence using tabular data

Velmurugan, M., Ouyang, C., Xu, Y., Sindhgatta, R., Wickramanayake, B., & Moreira, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109772 · Cited by 15

Causality-Aware Local Interpretable Model-Agnostic Explanations

Cinquini, M., & Guidotti, R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_6 · Cited by 11

Density-based reliable and robust explainer for counterfactual explanation

Zhang, S., Chen, X., Wen, S., & Li, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120214 · Cited by 13

Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

Ramírez, A., Berrios, M., Romero, J. R., & FELDT, R. H. (2023) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw58534.2023.00023 · Cited by 12

Narrative Creativity

Fletcher, A., & Benveniste, M. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009614801 · Cited by 13

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

Black Box Models for eXplainable Artificial Intelligence

Chennam, K. K., Mudrakola, S., Maheswari, V. U., Aluvalu, R., & Rao, K. G. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_1 · Cited by 10

Pitfalls of Explainable ML: An Industry PerspectivePreprint

Verma, S., Lahiri, A., Dickerson, J. P., & Lee, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07758 · Cited by 13

CUBE: Causal Intervention-Based Counterfactual Explanation for Prediction Models

Shao, X., Wang, H., Chen, X., Zhu, X., & Zhang, Y. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3322126 · Cited by 11

Responsible Artificial Intelligence for Mental Health Disorders: Current Applications and Future Challenges

El–Sappagh, S., Nazih, W., Alharbi, M., & Abuhmed, T. (2025) · Journal of Disability Research · doi:10.57197/jdr-2024-0101 · Cited by 10

LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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

Causality from bottom to top: a survey

Weinberg, A. I., Premebida, C., & Faria, D. R. (2025) · Machine Learning · doi:10.1007/s10994-025-06855-5 · Cited by 17

Machine learning in travel mode choice studies: A systematic literature review of applications, methods, and challenges

Hassan, M., Kabir, M. E., Akter, S. T., Shraban, S. S., Basaruddin, K. S., & Islam, M. A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108140 · Cited by 9

Mining Pareto-optimal counterfactual antecedents with a branch-and-bound model-agnostic algorithm

Raimundo, M. M., Nonato, L. G., & Poco, J. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00906-4 · Cited by 9

Robustness Implies Fairness in Causal Algorithmic Recourse

Ehyaei, A.-R., Karimi, A.-H., Schölkopf, B., & Maghsudi, S. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594057 · Cited by 10

Multi-modal anchor adaptation learning for multi-modal summarization

Chen, Z., Lu, Z., Rong, H., Zhao, C., & Xu, F. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127144 · Cited by 9

Interpretable ECG analysis for myocardial infarction detection through counterfactuals

Tanyel, T., Atmaca, S., Gökçe, K., Balık, M. Y., Güler, A., Aslanger, E. K., & Öksüz, İ. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107227 · Cited by 9

FishMet: A Digital Twin Framework for Appetite, Feeding Decisions and Growth in Salmonid Fish

Budaev, S. V., Cusimano, G. M., & Rønnestad, I. (2025) · Aquaculture Fish and Fisheries · doi:10.1002/aff2.70064 · Cited by 11

Models in Decision‐Making Under Risk and Uncertainty

Höppner, M. (2025) · Journal of Economic Surveys · doi:10.1111/joes.70008 · Cited by 14

Residual-informed deep meta regressor for predicting modal frequencies of historical masonry buildings under environmental and operational data scarcity

Entezami, A., Sarmadi, H., Kiarad, H., & Behkamal, B. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113675 · Cited by 8

Current Trends, Challenges and Techniques in XAI Field; A Tertiary Study of XAI Research

Brdnik, S., & Šumak, B. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569528 · Cited by 12

Sociology of Artificial Intelligence for Social Sustainability in the Digital Age

de Serpa, S. N. F., Mićić, L., Štilić, A., & Mastilo, Z. (2025) · Academic Journal of Interdisciplinary Studies · doi:10.36941/ajis-2025-0003 · Cited by 9

Self-Explanation as a Basis for Self-Integration - The Autonomous Passenger Ferry Scenario

Al-Falouji, G., Haschke, L., Nowotka, D., & Tomforde, S. (2023) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) · doi:10.1109/acsos-c58168.2023.00038 · Cited by 9

Understanding with Toy Surrogate Models in Machine Learning

Páez, A. (2024) · Minds and Machines · doi:10.1007/s11023-024-09700-1 · Cited by 12

Human performance effects of combining counterfactual explanations with normative and contrastive explanations in supervised machine learning for automated decision assistance

Gentile, D., Donmez, B., & Jamieson, G. A. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103434 · Cited by 8

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

Deep learning for breast cancer diagnosis: A bibliometric analysis and future research directions

Oladimeji, O. O., McLoughlin, I. V., & Unnikrishnan, S. (2024) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2024.100004 · Cited by 8

The role of IoT and XAI convergence in the prediction, explanation, and decision of customer perceived value (CPV) in SMEs: a theoretical framework and research proposition perspective

Abrokwah-Larbi, K. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00092-x · Cited by 7

Bidirectional transparency in human-agent communications: effects of direction and level of transparency

Xu, X., Yu, R., Yuan, M., & Zheng, J. (2025) · Ergonomics · doi:10.1080/00140139.2025.2456535 · Cited by 6

Towards Generating Counterfactual Examples as Automatic Short Answer Feedback

Filighera, A., Tschesche, J., Steuer, T., Tregel, T., & Wernet, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11644-5_17 · Cited by 7

Novel Actionable Counterfactual Explanations for Intrusion Detection Using Diffusion Models

Galwaduge, V., & Samarabandu, J. K. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5030068 · Cited by 6

Causality-enhanced system reliability and safety analysis: An overview

Zheng, S., Pan, K., Chen, Y., Liu, J., & Zio, E. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112109 · Cited by 6

Managing Artificial Intelligence Algorithmic Discrimination: The Internal Audit Function Role

Nzama, L. (2023) · Accounting, finance, sustainability, governance & fraud · doi:10.1007/978-981-99-6327-0_14 · Cited by 6

AI Explainability, Interpretability, Fairness, and Privacy: An Integrative Review of Reviews

Roundtree, A. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_19 · Cited by 6

Multi-agent Learning of Causal Networks in the Internet of Things

Mariani, S., Roseti, P., & Zambonelli, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37616-0_14 · Cited by 6

A Vision on What Explanations of Autonomous Systems are of Interest to Lawyers

Buiten, M. C., Dennis, L. A., & Schwammberger, M. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00062 · Cited by 10

The Importance of Time in Causal Algorithmic Recourse

Beretta, I., & Cinquini, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_16 · Cited by 6

Visual Analysis of Multi-Outcome Causal Graphs

Fan, M., Yu, J., Weiskopf, D., Cao, N., Wang, H., & Zhou, L. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456346 · Cited by 6

Debiasing weighted multi-view k-means clustering based on causal regularization

Huang, X., Tao, H., Ni, H., & Hou, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111195 · Cited by 6

Explainable artificial intelligence and the social sciences: a plea for interdisciplinary research

Mulder, W. D. (2024) · AI & Society · doi:10.1007/s00146-024-02058-z · Cited by 6

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

Kshetry, N., & Kantardzic, M. M. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.126011 · Cited by 5

Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-level Anomaly Detection

Xiao, C., Pang, S., Tai, W., Huang, Y., Trajcevski, G., & Zhou, F. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672050 · Cited by 6

Beyond the Black Box: XAI Strategies for Safeguarding Critical Infrastructure

Singh, A., Kathait, S., Kothari, A., Joshi, S., Agarwal, Y., Badoni, S., Bhatt, M., & Mishra, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-76473-8_7 · Cited by 6

Generating Purpose-Driven Explanations: The Case of Process Predictive Model Inspection

Wickramanayake, B., Ouyang, C., Moreira, C., & Xu, Y. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-07481-3_14 · Cited by 5

Counterfactual learning in enhancing resilience in autonomous agent systems

Samarasinghe, D. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1212336 · Cited by 5

Algorithms and Research in Accounting Application Based on Artificial Intelligence

Wang, X. (2023) · International Conference on Financial Innovation, FinTech and Information Technology (FFIT) · doi:10.4108/eai.7-7-2023.2338051 · Cited by 4

Counterfactual Explainer for Deep Reinforcement Learning Models using Policy Distillation

Samadi, A., Koufos, K., Debattista, K., & Dianati, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3709146 · Cited by 9

Counterfactuals in explainable artificial intelligence: Theory, algorithms, and applications

Chou, Y.-L. (. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.248281 · Cited by 5

Bridging the Human-AI Fairness Gap: How Providing Reasons Enhances the Perceived Fairness of Public Decision-MakingPreprint

Henning, A., & Langenbach, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4819145 · Cited by 5

Bayesian federated causal inference and its application in manufacturing

Xiao, X., Alharbi, K. A., Zhang, P., Qin, H., & Yue, X. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02665-7 · Cited by 6

Bridging the Human– AI Fairness Gap: How Providing Reasons Enhances the Perceived Fairness of Public Decision‐Making

Henning, A., & Langenbach, P. (2025) · Journal of Empirical Legal Studies · doi:10.1111/jels.70019 · Cited by 11

Explainable AI Does not Provide Reason Explanations

Graff, J. (2025) · Minds and Machines · doi:10.1007/s11023-025-09739-8 · Cited by 4

Generative Adversarial Network based Chest Disease Detection and Binary Mask Generation

Junaid, M. A., Anwar, S., Sikander, G., & Khan, M. T. (2023) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai57502.2023.10089542 · Cited by 4

Learning-based counterfactual explanations for recommendation

Wen, J., Liu, H., Jing, L., & Yu, J. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-3974-2 · Cited by 4

Commonsense for AI: an interventional approach to explainability and personalization

Farahmand, F. (2024) · AI & Society · doi:10.1007/s00146-024-02107-7 · Cited by 4

Evaluating example-based explainable artificial intelligence methods for breast cancer diagnosis

Xu, C., Zhu, P., Fan, Z., & Ma, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113222 · Cited by 4

Text-Based Causal Inference on Irony and Sarcasm Detection

Çekinel, R. F., & Karagöz, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12670-3_3 · Cited by 4

Opening the Black Box: Finding Osgood’s Semantic Factors in Word2vec Space

Суров, И. А. (2022) · Informatics and Automation · doi:10.15622/ia.21.5.3 · Cited by 5

Calico: Automated Knowledge Calibration and Diagnosis for Elevating AI Mastery in Code Tasks

Qiu, Y., Hu, J., Zhang, Q., & Yin, H. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680399 · Cited by 4

A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data

Shekhar, S., Salim, A., Jinturkar, V., Nayak, A., & Bansode, A. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487744 · Cited by 3

Empowering scientific discovery with explainable small domain-specific and large language models

Yu, H., Wang, Y., Cheng, T., Yan, Y., Dawson, K. A., Li, S. F. Y., Zheng, Y., & Jin, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11365-w · Cited by 4

Linking model intervention to causal interpretation in model explanation

Cheng, D., Xu, Z., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112814 · Cited by 6

Redefining Pathobiology with Spatial Multi-Omics at the Intersection of Biology, Computation, and Histopathological Assessment

Agrawal, A., & Thomann, S. (2026) · American Journal Of Pathology · doi:10.1016/j.ajpath.2026.01.007 · Cited by 4

Explaining Model Behavior with Global Causal Analysis

Robeer, M., Bex, F., Feelders, A., & Prakken, H. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_17 · Cited by 3

Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A data-morphology-based counterfactual generation method for trustworthy artificial intelligence

Pascual-Triana, J. D., Fernández, A., Ser, J. D., & Herrera, F. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121844 · Cited by 4

eXING-IoT conceptual framework for explainability integration in next generation-IoT

Vultureanu‐Albişi, A., Bădică, C., & Ivanović, M. D. (2025) · Connection Science · doi:10.1080/09540091.2025.2507180 · Cited by 5

Towards a unified framework for biodiversity action in the Triple Planetary Crisis

Zhou, J., Cricchio, J., Sylvester, F., Posthuma, L., Wilkes, M., Minin, A. D., Hollert, H., Brack, W., & Orsini, L. (2026) · Environment International · doi:10.1016/j.envint.2026.110205 · Cited by 3

A Quantitative Comparison of Causality and Feature Relevance via Explainable AI (XAI) for Robust, and Trustworthy Artificial Reasoning Systems

Rawal, A., McCoy, J., Raglin, A. J., & Rawat, D. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_17 · Cited by 3

AI Modelling of Counterfactual Thinking for Judicial Reasoning and Governance of Law

Pereira, L. M., Santos, F. C., & Lopes, A. B. (2023) · Law, governance and technology series · doi:10.1007/978-3-031-41264-6_14 · Cited by 3

Counterfactual and Causal Analysis for AI-Based Modulation and Coding Scheme Selection

Hao, K., Moysen, J., & Özger, M. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10464475 · Cited by 3

Distributed Discovery of Causal Networks in Pervasive Environments

Mariani, S., & Zambonelli, F. (2024) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops59983.2024.10502971 · Cited by 3

Characterizing the Impact of Physical Activity on Patients with Type 1 Diabetes Using Statistical and Machine Learning Models

Chushig-Muzo, D., Calero-Díaz, H., Fabelo, H. A., Årsand, E., van Dijk, P. R., & Soguero-Ruíz, C. (2024) · Applied Sciences · doi:10.3390/app14219870 · Cited by 4

Deference to opaque systems and morally exemplary decisions

Fritz, J. (2024) · AI & Society · doi:10.1007/s00146-024-02147-z · Cited by 3

Counterfactual contrastive learning for weakly supervised temporal sentence grounding

Xu, Y., Xu, W., & Miao, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129508 · Cited by 3

Amortized Generation of Sequential Counterfactual Explanations for Black-box Models.Preprint

Verma, S., Hines, K., & Dickerson, J. P. (2021) · arXiv (Cornell University) · Cited by 3

A Review of Evaluation Approaches for Explainable AI With Applications in CardiologyPreprint

Salih, A. M., Galazzo, I. B., Gkontra, P., Rauseo, E., Lee, A. M., Lekadir, K., Radeva, P. I., Petersen, S. E., & Menegaz, G. (2023) · doi:10.36227/techrxiv.24573304.v1 · Cited by 4

A Review of Evaluation Approaches for Explainable AI With Applications in CardiologyPreprint

Salih, A. M., Galazzo, I. B., Gkontra, P., Rauseo, E., Lee, A. M., Lekadir, K., Radeva, P. I., Petersen, S. E., & Menegaz, G. (2023) · doi:10.36227/techrxiv.24573304 · Cited by 3

Enhancing explainability via distinguishable feature learning based on causality in image classification

Yu, N., Chen, L., & Yi, X. (2024) · Displays · doi:10.1016/j.displa.2024.102933 · Cited by 2

Causal CSSE: integrating counterfactuals and causality in the explanation of machine learning models

Krauss, O. F. P., Balbino, M. D. S., & Nobre, C. N. (2025) · Machine Learning · doi:10.1007/s10994-025-06856-4 · Cited by 2

Causality, Explanations, Machine Learning, and Engineering

Naser, M. Z. (2025) · Foundations of Science · doi:10.1007/s10699-025-10006-3 · Cited by 2

Impact of EXplainable AI on Trust Evolution with AI Error Severity: Comparing Similar Instances and Saliency Map in a Baggage Screening Task

Wang, Y., Zhao, J. L., Ba, Y., Mancenido, M. V., Chiou, E. K., & Maciejewski, R. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2564272 · Cited by 2

Causal models for specifying requirements in industrial ML-based software: A case study

Heyn, H.-M., Mao, Y., Weiss, R. J., & Knauss, E. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112691 · Cited by 2

TagGAN: A generative model for data tagging

Nawaz, M., Nasir, B., Zia, T., Hussain, Z., & Moreira, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111385 · Cited by 2

Low-Cost Malware Detection with Artificial Intelligence on Single Board Computers

Steadman, P., Jenkins, P., Rathore, R. S., & Hewage, C. T. E. R. (2026) · Future Internet · doi:10.3390/fi18010046 · Cited by 4

The end of theory? AI and ignorance in financial markets

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Huang, F., Jiang, S., Shangguan, W., Li, L., Zhang, Y., Zhang, R., Li, Q., Li, D., & Dai, Y. (2026) · Science China Earth Sciences · doi:10.1007/s11430-025-1898-3 · Cited by 2

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Byrne, R. M. J. (1997) · ˜The œPsychology of learning and motivation/˜The œpsychology of learning and motivation · doi:10.1016/s0079-7421(08)60501-0 · Cited by 146

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