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

Explanation in artificial intelligence: Insights from the social sciences

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The Functional Theory of Counterfactual Thinking

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

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Open Set Learning with Counterfactual Images

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Counterfactual Thought

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

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The probability of causal conditionals

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Explainable Artificial Intelligence in Genomic Sequence for Healthcare Systems Prediction

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Explainability-Aware One Point Attack for Point Cloud Neural Networks

Tan, H., & Kotthaus, H. (2021) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00456 · Cited by 23

Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework

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Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars

Lampridis, O., Guidotti, R., & Ruggieri, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61527-7_24 · Cited by 20

Using state abstractions to compute personalized contrastive explanations for AI agent behavior

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Ask to Know More

Dai, S.-C., Hsu, Y.-L., Xiong, A., & Ku, L. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539205 · Cited by 18

Can counterfactual explanations of AI systems’ predictions skew lay users’ causal intuitions about the world? If so, can we correct for that?

Tešić, M., & Hahn, U. (2022) · Patterns · doi:10.1016/j.patter.2022.100635 · Cited by 18

“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

The Suppression of Inferences From Counterfactual Conditionals

Espino, O., & Byrne, R. M. J. (2020) · Cognitive Science · doi:10.1111/cogs.12827 · Cited by 16

Explaining black-box classifiers: Properties and functions

Leila Amgoud, A. (2023) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2023.01.004 · Cited by 15

Explainable AI as evidence of fair decisions

Leben, D. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1069426 · Cited by 15

Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

Artelt, A., & Hammer, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.129 · Cited by 30

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Mollaei, N., Fujão, C., Silva, L., Rodrigues, J., Cepeda, C., & Gamboa, H. F. S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159552 · Cited by 14

Model-contrastive explanations through symbolic reasoning

Malandri, L., Mercorio, F., Mezzanzanica, M., & Seveso, A. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114040 · Cited by 15

The AI-DEC: A Card-based Design Method for User-centered AI Explanations

Lee, C. P., Lee, M. K., & Mutlu, B. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661576 · Cited by 19

Categorical and Continuous Features in Counterfactual Explanations of AI Systems

Warren, G., Byrne, R. M. J., & Keane, M. T. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3673907 · Cited by 16

Explainable artificial intelligence and social science: Further insights for qualitative investigation

Johs, A. J., Agosto, D. E., & Weber, R. O. (2022) · Applied AI Letters · doi:10.1002/ail2.64 · Cited by 25

Explainable AI for Fighting COVID-19 Pandemic: Opportunities, Challenges, and Future Prospects

Abiodun, M. K., Awotunde, J. B., Aremu, D. R., & Adeniyi, A. E. (2022) · Disruptive technologies and digital transformations for society 5.0 · doi:10.1007/978-981-16-3783-4_15 · Cited by 17

Towards Explainability of Tree-Based Ensemble Models. A Critical Overview

Sepioło, D., & Ligęza, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-06746-4_28 · Cited by 14

Distal Explanations for Model-free Explainable Reinforcement LearningPreprint

Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10284 · Cited by 14

Counterfactual Explanations in Explainable AI: A Tutorial

Wang, C., Li, X., Han, H., Wang, S., Wang, L., Cao, C. C., & Chen, L. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470797 · Cited by 16

“If it Didn't Happen, Why Would I Change My Decision?”: How Judges Respond to Counterfactual Explanations for the Public Safety Assessment

Yacoby, Y., Green, B., Griffin, C. L., Jr., & Doshi‐Velez, F. (2022) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v10i1.22001 · Cited by 20

Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and SolutionsPreprint

Delaney, E. D., Greene, D., & Keane, M. T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09734 · Cited by 13

Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo, M., & Luengo, D. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_1 · Cited by 15

Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI

Aryal, S., & Keane, M. T. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/732 · Cited by 12

“I do not know! but why?” — Local model-agnostic example-based explanations of reject

Artelt, A., Visser, R., & Hammer, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126722 · Cited by 16

Counterfactual Thinking in Cooperation Dynamics

Pereira, L. M., & Santos, F. C. (2019) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-030-32722-4_5 · Cited by 12

Distal Explanations for Explainable Reinforcement Learning AgentsPreprint

Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020) · arXiv (Cornell University) · Cited by 12

Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation System

Jeon, G., Kim, S., & Lee, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2238369 · 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

Model-agnostic counterfactual explanation: A feature weights-based comprehensive causal multi-objective counterfactual framework

Liu, J., Wu, X., Liu, S., & Gong, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126063 · Cited by 14

Explaining short text classification with diverse synthetic exemplars and counter-exemplars

Lampridis, O., State, L., Guidotti, R., & Ruggieri, S. (2022) · Machine Learning · doi:10.1007/s10994-022-06150-7 · Cited by 13

BayCon: Model-agnostic Bayesian Counterfactual Generator

Romashov, P., Gjoreski, M., Sokol, K., Martínez, M. V., & Langheinrich, M. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/104 · Cited by 11

Distributed Learning in Healthcare

Tuladhar, A., Rajashekar, D., & Forkert, N. D. (2022) · Integrated science · doi:10.1007/978-3-031-11199-0_10 · Cited by 11

Human performance consequences of normative and contrastive explanations: An experiment in machine learning for reliability maintenance

Gentile, D., Donmez, B., & Jamieson, G. A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103945 · Cited by 13

Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice

Cabitza, F., Campagner, A., Famiglini, L., Natali, C., Caccavella, V. M., & Gallazzi, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40837-3_10 · Cited by 15

Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisions

Praveen, S., & Joshi, K. K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-18292-1_6 · Cited by 10

Enhanced Network Intrusion Detection Systems With Explainable Artificial Intelligence for Network Security

Dasari, A. K., Biswas, S. K., & Purkayastha, B. (2025) · International Journal of Communication Systems · doi:10.1002/dac.70209 · Cited by 10

Evaluating Prebunking and Nudge Techniques in Tackling Misinformation: A Between-Subject Study on Social Media Platforms

Barman, D., & Conlan, O. (2024) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3648188.3675125 · Cited by 9

3Es for AI: Economics, Explanation, Epistemology

Kaul, N. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.833238 · Cited by 9

Explaining Model Confidence Using Counterfactuals

Le, T. N., Miller, T., Singh, R., & Sonenberg, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26399 · Cited by 15

Finding AI’s Faults with AAR/AI: An Empirical Study

Khanna, R., Dodge, J. E., Anderson, A. W., Dikkala, R., Irvine, J., Shureih, Z., Lam, K.-H., Matthews, C., Lin, Z., Kahng, M., et al. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3487065 · Cited by 10

This Changes to That : Combining Causal and Non-Causal Explanations to Generate Disease Progression in Capsule Endoscopy

Vats, A., Mohammed, A. M., Pedersen, M., & Wiratunga, N. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096931 · Cited by 9

Preference Elicitation in Interactive and User-centered Algorithmic Recourse: an Initial Exploration

Esfahani, S., Toni, G. D., Lepri, B., Passerini, A., Tentori, K., & Zancanaro, M. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3627043.3659556 · Cited by 8

AI Fairness

Leben, D. (2025) · The MIT Press eBooks · doi:10.7551/mitpress/15740.001.0001 · Cited by 8

I Wish I Would Have Loved This One, But I Didn’t – A Multilingual Dataset for Counterfactual Detection in Product Review

O’Neill, J., Rozenshtein, P., Kiryo, R., Kubota, M., & Bollegala, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.568 · Cited by 33

Machine Reasoning ExplainabilityPreprint

Čyras, K., Badrinath, R., Mohalik, S. K., Mujumdar, A. P., Nikou, A., Previti, A., Sundararajan, V., & Feljan, A. V. (2020) · arXiv (Cornell University) · doi:10.4230/lipics.cp.2026.62 · Cited by 19

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

Assessing the Transparency and Explainability of AI Algorithms in Planning and Scheduling tools: A Review of the Literature

Morandini, S., Fraboni, F., Balatti, E., Hackmann, A., Brendel, H., Puzzo, G., Volpi, L., Giusino, D., Angelis, M. D., & Pietrantoni, L. (2023) · AHFE international · doi:10.54941/ahfe1004068 · Cited by 7

For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI

Kuhl, U., Artelt, A., & Hammer, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_14 · Cited by 9

Using Counterfactuals to Improve Causal Inferences From Visualizations

Borland, D., Wang, A. Z., & Gotz, D. (2024) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2023.3338788 · Cited by 12

Explaining Black-box Classification Models with Arguments

Amgoud, L. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00126 · Cited by 7

Situated conditional reasoning

Casini, G., Meyer, T. A., & Varzinczak, I. J. ́. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103917 · Cited by 7

Explaining the Influence of Prior Knowledge on POMCP Policies

Castellini, A., Marchesini, E., Mazzi, G., & Farinelli, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66412-1_17 · Cited by 7

Contrastive Explanations for Explaining Model Adaptations

Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85030-2_9 · Cited by 7

Qualitative investigation in explainable artificial intelligence: A bit more insight from social sciencePreprint

Johs, A. J., Agosto, D. E., & Weber, R. O. (2021) · doi:10.22541/au.163284810.09140868/v1 · Cited by 7

When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making

Kuhl, U., & Bush, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08333-3_12 · Cited by 6

Contrasting Explanations for Understanding and Regularizing Model Adaptations

Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10826-5 · Cited by 6

Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI

Warren, G., Delaney, E. D., Guéret, C., & Keane, M. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63646-2_14 · Cited by 6

On Glocal Explainability of Graph Neural Networks

Lv, G., Chen, L., & Cao, C. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-00123-9_52 · Cited by 7

Robust Explanations for Human-Neural Multi-agent Systems with Formal Verification

Leofante, F., & Lomuscio, A. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43264-4_16 · Cited by 5

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

How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations

Wijekoon, A., Wiratunga, N., Nkisi-Orji, I., Palihawadana, C., Corsar, D., & Martin, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_3 · Cited by 6

Model Agnostic Local Explanations of Reject

Artelt, A., Visser, R., & Hammer, B. (2022) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2022.es2022-34 · Cited by 8

Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy CowsPreprint

Ryan, C., Guéret, C., Berry, D. P., Corcoran, M., Keane, M. T., & Namee, B. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02188 · Cited by 6

Counterfactual Explanations as Interventions in Latent SpacePreprint

Crupi, R., Castelnovo, A., Regoli, D., & González, B. S. M. (2021) · Research Square · doi:10.21203/rs.3.rs-626875/v1 · Cited by 6

Deciphering the black box: interactive crop recommendation system using Explainable AI with visualisation dashboards

Akkem, Y., Biswas, S. K., & Aruna, V. (2025) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2025.2595024 · Cited by 5

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 7

Subjectivity of Explainable Artificial Intelligence

Raikov, A. (2022) · Russian Journal of Philosophical Sciences · doi:10.30727/0235-1188-2022-65-1-72-90 · Cited by 5

A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information

Milani, R., Moll, M., Leone, R. D., & Pickl, S. (2023) · Sensors · doi:10.3390/s23042013 · Cited by 5

Counterfactuals as modal conditionals, and their probability

Rosella, G., Flaminio, T., & Bonzio, S. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103970 · Cited by 5

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

Evolving Counterfactual Explanations with Particle Swarm Optimization and Differential Evolution

Andersen, H., Lensen, A., Browne, W. N., & Mei, Y. (2022) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec55065.2022.9870283 · Cited by 5

Explainable AI for Sentiment Analysis

Pavitha, N., Ratnaparkhi, P., Uzair, A., More, A., Raj, S., & Yadav, P. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-3571-8_41 · Cited by 5

Causal explanation for reinforcement learning: quantifying state and temporal importance

Wang, X., Meng, F., Liu, X., Kong, Z., & Chen, X. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04649-7 · Cited by 7

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

Let Me Explain What I Did or What I Would Have Done: An Empirical Study on the Effects of Explanations and Person-Likeness on Trust in and Understanding of Algorithms

Szczuka, J. M., Horstmann, A. C., Szymczyk, N., Strathmann, C., Artelt, A., Mavrina, L., & Krämer, N. C. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685351 · Cited by 6

Enhancing Safety and Reliability in Vanets for Autonomous Vehicles by M-XAI (Multi-modal Explainable-AI)

Gupta, U. C., Pranav, A., Dubey, A., Modi, R. K., & Singh, A. (2025) · Springer eBooks · doi:10.1007/978-981-96-2355-6_15 · Cited by 5

JUICE: JUstIfied Counterfactual Explanations

Kuratomi, A., Miliou, I., Lee, Z., Lindgren, T., & Papapetrou, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18840-4_35 · Cited by 5

Computational Accountability

Hulstijn, J. (2023) · International Conference on Artificial Intelligence and Law · doi:10.1145/3594536.3595122 · Cited by 6

Understanding User Preferences in Explainable Artificial Intelligence: A Mapping Function Proposal

Hashemi, M., Darejeh, A., & Cruz, F. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3733837 · Cited by 11

Human Factors in AI-Driven Cybersecurity: Cognitive Biases and Trust Issues

Hagen, R. A., Øverlier, L., & Helkala, K. M. (2025) · Digital Threats Research and Practice · doi:10.1145/3759260 · Cited by 6

Improving Explainability of Integrated Gradients with Guided Non-Linearity

Kwon, H. J., Koo, H. I., & Cho, N. I. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412936 · Cited by 4

Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings

Marinho, L. B., da Costa, J. B. G., Parra, D., & Santos, R. L. T. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511104 · Cited by 4

COIN: Counterfactual Inpainting for Weakly Supervised Semantic Segmentation for Medical Images

Shvetsov, D., Ariva, J., Domnich, M., Vicente, R., & Fishman, D. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_3 · Cited by 4

Generating Local Textual Explanations for CNNs: A Semantic Approach Based on Knowledge Graphs

Horta, V., & Mileo, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08421-8_37 · Cited by 4

Influence of context on users’ views about explanations for decision-tree predictions

Maruf, S., Ingrid Zukerman, A., Reiter, E., & Haffari, G. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101483 · Cited by 4

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

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

Paving the way towards counterfactual generation in argumentative conversational agents

Stepin, I., Catalá, A., Pereira-Fariña, M., & Alonso, J. M. (2019) · doi:10.18653/v1/w19-8405 · Cited by 4

PROBABILISTIC COUNTERFACTUAL CAUSAL MODEL FOR A SINGLE INPUT VARIABLE IN EXPLAINABILITY TASK

Chalyi, S. F., & Leshchynskyi, V. O. (2023) · Advanced Information Systems · doi:10.20998/2522-9052.2023.3.08 · Cited by 4

Counterfactual Explanations of Concept DriftPreprint

Hinder, F., & Hammer, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12822 · Cited by 6

I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product ReviewsPreprint

O’Neill, J., Rozenshtein, P., Kiryo, R., Kubota, M., & Bollegala, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06893 · Cited by 4

Explainable artificial intelligence (XAI) for healthcare decision-making

Zeng, Z. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/155849 · Cited by 4

Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data

Tonati, S., Vece, M. D., Giannotti, F., & Pellungrini, R. (2025) · Machine Learning · doi:10.1007/s10994-025-06880-4 · Cited by 3

Illuminating Industry Evolution: Reframing Artificial Intelligence Through Transparent Machine Reasoning

Rosário, A. T., & Dias, J. C. (2025) · Information · doi:10.3390/info16121044 · Cited by 3

Ijuice: integer JUstIfied counterfactual explanations

Kuratomi, A., Miliou, I., Lee, Z., Lindgren, T., & Papapetrou, P. (2024) · Machine Learning · doi:10.1007/s10994-024-06530-1 · Cited by 3

ExplainLFS: Explaining neural architectures for similarity learning from local perturbations in the latent feature space

Bello, M., Costa, P., Nápoles, G., Mesejo, P., & Cordón, Ó. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102407 · Cited by 3

Framework for the application of explainable artificial intelligence techniques in the service of democracy

da Encarnacao, M. S. M., Anastasiadou, M., & Santos, V. (2024) · Transforming Government People Process and Policy · doi:10.1108/tg-02-2024-0030 · Cited by 3

Designing a pragmatic explanation for the XAI system based on the user's context and background knowledge

Kim, S., Huh, I., Park, Y., & Lee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85648-5.00012-8 · Cited by 3

Using Siamese Neural Networks for the Open Set Recognition of Anomalies Detected in Industrial Time Series Data

Dix, M., Koltermann, J. J., Mieck, S., Petersen, H., Taege, S., & Anjanappa, G. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10710747 · Cited by 3

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