Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning

Explanation in artificial intelligence: Insights from the social sciences

Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302

The experience of regret: What, when, and why.

Gilovich, T., & Medvec, V. H. (1995) · Psychological Review · doi:10.1037/0033-295x.102.2.379 · Cited by 1,098

The Functional Theory of Counterfactual Thinking

Epstude, K., & Roese, N. J. (2008) · Personality and Social Psychology Review · doi:10.1177/1088868308316091 · Cited by 905

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

The rational imagination: how people create alternatives to reality

(2006) · Choice Reviews Online · doi:10.5860/choice.43-2737 · Cited by 603

When less is more: Counterfactual thinking and satisfaction among Olympic medalists.

Medvec, V. H., Madey, S. F., & Gilovich, T. D. (1995) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.69.4.603 · Cited by 526

Open Set Learning with Counterfactual Images

Neal, L., Olson, M., Fern, X. Z., Wong, W., & Li, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_38 · Cited by 510

Counterfactual Thought

Byrne, R. M. J. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033249 · Cited by 467

The Lessons We (Don't) Learn: Counterfactual Thinking and Organizational Accountability after a Close Call

Morris, M. W., & Moore, P. C. (2000) · Administrative Science Quarterly · doi:10.2307/2667018 · Cited by 268

The probability of causal conditionals

Over, D. E., Hadjichristidis, C., Evans, J. S. B. T., Handley, S. J., & Sloman, S. A. (2007) · Cognitive Psychology · doi:10.1016/j.cogpsych.2006.05.002 · Cited by 229

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 Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762

Explainable artificial intelligence: an analytical review

Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1424 · Cited by 843

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652

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 583

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376

Factual and Counterfactual Explanations for Black Box Decision Making

Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., & Turini, F. (2019) · IEEE Intelligent Systems · doi:10.1109/mis.2019.2957223 · Cited by 349

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 422

Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making

Wang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320

Bias and Discrimination in AI: A Cross-Disciplinary Perspective

Ferrer, X., Nuenen, T. V., Such, J. M., Cote, M., & Criado, N. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056293 · Cited by 308

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312

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

Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302

Knowledge graphs as tools for explainable machine learning: A survey

Tiddi, I., & Stefan Schlobach, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103627 · Cited by 287

Benchmarking and survey of explanation methods for black box models

Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 410

Counterfactual Explanations for Machine Learning: A Review.Preprint

Verma, S., Dickerson, J. P., & Hines, K. (2020) · arXiv (Cornell University) · Cited by 223

Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies

Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211

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 245

Explainable Artificial Intelligence for Intrusion Detection System

Patil, S., Vijayakumar, V., Mazhar, S. M., Sahibzada, A., Ahmed, N., Sinha, O., Kumar, S., Shaw, K., & Kotecha, K. V. (2022) · Electronics · doi:10.3390/electronics11193079 · Cited by 226

A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023) · ACM Computing Surveys · doi:10.1145/3527848 · Cited by 301

Interpretable and explainable machine learning: A methods‐centric overview with concrete examples

Marcinkevičs, R., & Vogt, J. E. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1493 · Cited by 210

If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques

Keane, M. T., Kenny, E. M., Delaney, E. D., & Smyth, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/609 · Cited by 203

Adversarial XAI Methods in Cybersecurity

Kuppa, A., & Le‐Khac, N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3117075 · Cited by 151

A survey on XAI and natural language explanations

Cambria, E., Malandri, L., Mercorio, F., Mezzanzanica, M., & Nobani, N. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2022.103111 · Cited by 131

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 135

EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124

On generating trustworthy counterfactual explanations

Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119898 · Cited by 122

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445088 · Cited by 117

GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning

Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.825565 · Cited by 119

Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming

Akkem, Y., Biswas, S. K., & Varanasi, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10208-z · Cited by 108

Instance-Based Counterfactual Explanations for Time Series Classification

Delaney, E. D., Greene, D., & Keane, M. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86957-1_3 · Cited by 109

On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

Kenny, E. M., & Keane, M. T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17377 · Cited by 108

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 103

Brain at Work and in Everyday Life as the Next Frontier: Grand Field Challenges for Neuroergonomics

Dehais, F., Karwowski, W., & Ayaz, H. (2020) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2020.583733 · Cited by 91

Explainable Artificial Intelligence: a Systematic Review

Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269

Cross-View Geo-Localization via Learning Disentangled Geometric Layout Correspondence

Zhang, X., Li, X., Sultani, W., Zhou, Y. X., & Wshah, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25457 · Cited by 89

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 102

“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems

Riveiro, M., & Thill, S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103507 · Cited by 93

The possibilities and limits of explicable artificial intelligence (XAI) in education: a socio-technical perspective

Farrow, R. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2023.2185630 · Cited by 81

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 92

A survey of algorithmic recourse: definitions, formulations, solutions, and prospectsPreprint

Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04050 · Cited by 74

Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems

Yang, Q., Hao, Y., Quan, K., Yang, S., Zhao, Y., Kuleshov, V., & Wang, F. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581393 · Cited by 92

Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making

Lee, M. H., & Chew, C. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610218 · Cited by 78

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

Explainability pitfalls: beyond dark patterns in explainable AI

Ehsan, U., & Riedl, M. O. (2024) · Patterns · doi:10.1016/j.patter.2024.100971 · Cited by 72

On the role of artificial intelligence in medical imaging of COVID-19

Born, J., Beymer, D., Rajan, D., Coy, A., Mukherjee, V. V., Manica, M., Prasanna, P. M., Ballah, D., Guindy, M., Shaham, D., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100269 · Cited by 73

Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

Yang, L., Kenny, E. M., Ng, T. L. J., Yang, Y., Smyth, B., & Dong, R. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.541 · Cited by 84

NICE: an algorithm for nearest instance counterfactual explanations

Brughmans, D., Leyman, P., & Martens, D. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00930-y · Cited by 64

Explainability and white box in drug discovery

Kırboğa, K. K., Abbasi, S. W., & Küçüksille, E. U. (2023) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14262 · Cited by 62

Explainable Reinforcement Learning through a Causal Lens

Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5631 · Cited by 58

The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples

Freiesleben, T. (2021) · Minds and Machines · doi:10.1007/s11023-021-09580-9 · Cited by 76

Behavioral Use Licensing for Responsible AI

Contractor, D., McDuff, D., Haines, J. K., Lee, J., Hines, C., Hecht, B., Vincent, N., & Li, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533143 · Cited by 54

Making AI Explainable in the Global South: A Systematic Review

Okolo, C. T., Dell, N. L., & Vashistha, A. (2022) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3530190.3534802 · Cited by 59

Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction

Wu, Y., Yang, H., Lin, Y. H., & Liu, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3286690 · Cited by 54

Model-based explanations of concept drift

Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126640 · Cited by 62

Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps

Huber, T., Weitz, K., André, E., & Amir, O. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103571 · Cited by 53

Leveraging explanations in interactive machine learning: An overview

Teso, S., Alkan, Ö., Stammer, W., & Daly, E. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1066049 · Cited by 76

Explainable AI and Causal Understanding: Counterfactual Approaches Considered

Baron, S. (2023) · Minds and Machines · doi:10.1007/s11023-023-09637-x · Cited by 58

How AI tools can—and cannot—help organizations become more ethical

Cremer, D. D., & Narayanan, D. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1093712 · Cited by 64

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

Prado-Romero, M. A., Prenkaj, B., Stilo, G., & Giannotti, F. (2023) · ACM Computing Surveys · doi:10.1145/3618105 · Cited by 60

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository · doi:10.17863/cam.69929 · Cited by 49

Explaining Any Time Series Classifier

Guidotti, R., Monreale, A., Spinnato, F., Pedreschi, D., & Giannotti, F. (2020) · 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi50398.2020.00029 · Cited by 46

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

Thiagarajan, J. J., Thopalli, K., Rajan, D., & Turaga, P. K. (2022) · Scientific Reports · doi:10.1038/s41598-021-04529-5 · Cited by 50

Evaluating Robustness of Counterfactual Explanations

Artelt, A., Vaquet, V., Velioğlu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660058 · Cited by 43

Counterfactual Explanations for Prediction and Diagnosis in XAI

Dai, X., Keane, M. T., Shalloo, L., Ruelle, E., & Byrne, R. M. J. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534144 · Cited by 51

Explainable AI: The Effect of Contradictory Decisions and Explanations on Users’ Acceptance of AI Systems

Ebermann, C., Selisky, M., & Weibelzahl, S. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2126812 · Cited by 54

Directive Explanations for Actionable Explainability in Machine Learning Applications

Singh, R., Miller, T., Lyons, H., Sonenberg, L., Velloso, E., Vetere, F., Howe, P. D. L., & Dourish, P. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579363 · Cited by 57

Principles of Explainable Artificial Intelligence

Guidotti, R., Monreale, A., Pedreschi, D., & Giannotti, F. (2021) · Springer eBooks · doi:10.1007/978-3-030-76409-8_2 · Cited by 47

Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis

Yang, L., Li, J., Cunningham, P., Zhang, Y., Smyth, B., & Dong, R. (2021) · doi:10.18653/v1/2021.acl-long.26 · Cited by 52

Explainable software systems: from requirements analysis to system evaluation

Chazette, L., Brunotte, W., & Speith, T. (2022) · Requirements Engineering · doi:10.1007/s00766-022-00393-5 · Cited by 52

Unsupervised Concept Drift Detection From Deep Learning Representations in Real-Time

Greco, S., Vacchetti, B., Apiletti, D., & Cerquitelli, T. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3593123 · Cited by 49

How people reason with counterfactual and causal explanations for Artificial Intelligence decisions in familiar and unfamiliar domains

Celar, L., & Byrne, R. M. J. (2023) · Memory & Cognition · doi:10.3758/s13421-023-01407-5 · Cited by 45

A historical perspective of biomedical explainable AI research

Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100830 · Cited by 46

GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement

Zhang, X., Li, X., Sultani, W., Chen, C., & Wshah, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443652 · Cited by 44

Natural Example-Based Explainability: A Survey

Poché, A., Hervier, L., & Bakkay, M.-C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_2 · Cited by 33

Machine learning and structural econometrics: contrasts and synergies

Iskhakov, F., Rust, J. P., & Schjerning, B. (2020) · Econometrics Journal · doi:10.1093/ectj/utaa019 · Cited by 35

Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction

Cho, S. H., & Shin, K.-S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119390 · Cited by 38

Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data

Mikołajczyk, A., Grochowski, M., & Kwasigroch, A. (2020) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2021-0004 · Cited by 35

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

Oliveira, R., & Martens, D. (2021) · Applied Sciences · doi:10.3390/app11167274 · Cited by 36

A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations

Smyth, B., & Keane, M. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_2 · Cited by 30

Categorical and Continuous Features in Counterfactual Explanations of AI Systems

Warren, G., Byrne, R. M. J., & Keane, M. T. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584090 · Cited by 29

Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

Igami, M. (2020) · The Econometrics Journal · doi:10.1093/ectj/utaa005 · Cited by 28

Counterfactual Explanations for Predictive Business Process Monitoring

Huang, T.-H., Metzger, A., & Pohl, K. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-95947-0_28 · Cited by 28

Counterfactual explanation generation with minimal feature boundary

You, D., Niu, S., Dong, S., Yan, H., Chen, Z., Wu, D., Shen, L., & Wu, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.012 · Cited by 27

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities

Gajcin, J., & Dusparić, I. (2024) · ACM Computing Surveys · doi:10.1145/3648472 · Cited by 36

Post-hoc Explanation Options for XAI in Deep Learning: The Insight Centre for Data Analytics Perspective

Kenny, E. M., Delaney, E. D., Greene, D., & Keane, M. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_2 · Cited by 24

Improving Visualization Interpretation Using Counterfactuals

Kaul, S., Borland, D., Cao, N., & Gotz, D. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114779 · Cited by 23

Counterfactual explanations as interventions in latent space

Crupi, R., Castelnovo, A., Regoli, D., & González, B. S. M. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00889-2 · Cited by 23

Counterfactual explanations for misclassified images: How human and machine explanations differ

Delaney, E. D., Pakrashi, A., Greene, D., & Keane, M. T. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103995 · Cited by 28

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 34

Formalising the Robustness of Counterfactual Explanations for Neural Networks

Jiang, J., Leofante, F., Rago, A., & Toni, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26740 · Cited by 27

Generating Realistic Natural Language Counterfactuals

Robeer, M., Bex, F., & Feelders, A. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.306 · Cited by 29

Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context

Kim, S., Choo, S., Park, D., Park, H., Nam, C. S., Jung, J., & Lee, S. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103009 · Cited by 23

How Do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

Schrills, T., & Franke, T. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3588594 · Cited by 26

Incremental XAI: Memorable Understanding of AI with Incremental Explanations

Bo, J. Y., Hao, P., & Lim, B. Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642689 · Cited by 28

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Kuhl, U., Artelt, A., & Hammer, B. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1087929 · Cited by 20

Time Series Model Attribution Visualizations as Explanations

Schlegel, U., & Keim, D. A. (2021) · IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) · doi:10.1109/trex53765.2021.00010 · Cited by 23

Introducing contextual transparency for automated decision systems

Sloane, M., Solano-Kamaiko, I. R., Yuan, J., Dasgupta, A., & Stoyanovich, J. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00623-7 · Cited by 22

Understanding Any Time Series Classifier with a Subsequence-based Explainer

Spinnato, F., Guidotti, R., Monreale, A., Nanni, M., Pedreschi, D., & Giannotti, F. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3624480 · Cited by 19

Explainability Pitfalls: Beyond Dark Patterns in Explainable AI

Ehsan, U., & Riedl, M. O. (2021) · arXiv · doi:10.48550/arxiv.2109.12480 · Cited by 99

Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)

Keane, M. T., & Smyth, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58342-2_11 · Cited by 158

Explainable AI and Causal Understanding: Counterfactual Approaches Considered

Baron, S. (2023) · Minds and Machines · doi:10.1007/s11023-023-09637-x · Cited by 58

Counterfactual Explanations in AI

Priya, B., Kamisetty, S. S. S., Guptha, A. B. G. N. V., & Kavyasree, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/406020

User-centric explainable AI: design and evaluation of an approach to generate coherent counterfactual explanations for structured data

Förster, M., Hühn, P., Klier, M., & Kluge, K. (2022) · Journal of Decision System · doi:10.1080/12460125.2022.2119707 · Cited by 12

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

“Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI)

Warren, G., Smyth, B., & Keane, M. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_5 · Cited by 17