ViCE: visual counterfactual explanations for machine learning models

A Unified Approach to Interpreting Model Predictions

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

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works

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 · see citing works

Explaining machine learning classifiers through diverse counterfactual explanations

Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233 · see citing works

Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus

Smith, J. W., Everhart, J. E., Dickson, W., Knowler, W. C., & Johannes, R. S. (1988) · PubMed Central · Cited by 813 · see citing works

iml: An R package for Interpretable Machine Learning

Molnar, C. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00786 · Cited by 720 · see citing works

The What-If Tool: Interactive Probing of Machine Learning Models

Wexler, J., Pushkarna, M., Bolukbasi, T., WATTENBERG, M. P., Viégas, F. B., & Wilson, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934619 · Cited by 512 · see citing works

Interpretable Machine Learning

Molnar, C. (2020) · Cited by 506 · 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

Classification of radar returns from the ionosphere using neural networks

Sigillito, V. G. (1989) · Johns Hopkins APL technical digest · Cited by 347 · see citing works

Rulematrix: Visualizing and understanding classifiers with rules

Ming, Y., Qu, H., & Bertini, E. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864812 · Cited by 243 · see citing works

EnsembleMatrix

Talbot, J., Lee, B., Kapoor, A., & Tan, D. S. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518895 · Cited by 226 · see citing works

Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

Zhang, J., Wang, Y., Molino, P., Li, L., & Ebert, D. S. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864499 · Cited by 222 · see citing works

A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations

Krause, J., Dasgupta, A., Swartz, J. L., Aphinyanaphongs, Y., & Bertini, E. (2017) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2017.8585720 · Cited by 87 · see citing works

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

Tamagnini, P., Krause, J., Dasgupta, A., & Bertini, E. (2017) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3077257.3077260 · Cited by 70 · see citing works

Explaining Data-Driven Document Classifications

Martens, D., & Provost, F. J. (2013) · Faculty Digital Archive (New York University Florence) · Cited by 11 · see citing works

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958 · see citing works

Evaluating the quality of machine learning explanations: a survey on methods and metrics

Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works

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 497 · see citing works

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 · see citing works

Explainable artificial intelligence (XAI) in finance: a systematic literature review

Černevičienė, J., & Kabašinskas, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10854-8 · Cited by 458 · see citing works

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 · see citing works

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267 · see citing works

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 456 · see citing works

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 · see citing works

Visual, textual or hybrid: the effect of user expertise on different explanations

Szymanski, M., Millecamp, M., & Verbert, K. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450662 · Cited by 131 · see citing works

Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles

Popolin, M., & Paulovich, F. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030354 · Cited by 107 · see citing works

Fairness issues, current approaches, and challenges in machine learning models

Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 91 · see citing works

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 · see citing works

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 · see citing works

Contextualization and Exploration of Local Feature Importance Explanations to Improve Understanding and Satisfaction of Non-Expert Users

Bove, C., Aigrain, J., Lesot, M., Tijus, C., & Detyniecki, M. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511139 · Cited by 68 · see citing works

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations

Chen, C., Lin, K., Rudin, C. D., Shaposhnik, Y., Wang, S., & Wang, T. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113647 · Cited by 63 · see citing works

Fighting Money Laundering With Statistics and Machine Learning

Jensen, R. I. T., & Iosifidis, A. (2023) · IEEE Access · doi:10.1109/access.2023.3239549 · Cited by 61 · see citing works

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual ApproachPreprint

Fernández-Loría, C., Provost, F. J., & Han, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07417 · Cited by 49 · see citing works

Visualization for Recommendation Explainability: A Survey and New Perspectives

Chatti, M. A., Guesmi, M., & Muslim, A. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3672276 · Cited by 56 · see citing works

Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making

Hoque, M. N., & Mueller, K. D. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3102051 · Cited by 43 · see citing works

A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making

Salimzadeh, S., He, G., & Gadiraju, U. (2023) · Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3565472.3592959 · Cited by 42 · see citing works

Model-Agnostic Counterfactual Explanations in Credit Scoring

Dastile, X., Çelik, T., & Vandierendonck, H. (2022) · IEEE Access · doi:10.1109/access.2022.3177783 · Cited by 41 · see citing works

Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making

Salimzadeh, S., He, G., & Gadiraju, U. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641905 · Cited by 41 · see citing works

DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps

Bertucci, D., Hamid, M. M., Anand, Y., Ruangrotsakun, A., Tabatabai, D., Perez, M., & Kahng, M. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209425 · Cited by 38 · see citing works

The Role of Human Knowledge in Explainable AI

Tocchetti, A., & Brambilla, M. (2022) · Data · doi:10.3390/data7070093 · Cited by 39 · see citing works

Interpretable selective learning in credit risk

Chen, D., Ye, J., & Ye, W. (2023) · Research in International Business and Finance · doi:10.1016/j.ribaf.2023.101940 · Cited by 31 · see citing works

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Veldhuis, M. S., Ariëns, S., Ypma, R. J. F., Abeel, T., & Benschop, C. C. G. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102632 · Cited by 28 · see citing works

PDPilot: Exploring Partial Dependence Plots Through Ranking, Filtering, and Clustering

Kerrigan, D., Barr, B., & Bertini, E. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3545025 · Cited by 27 · see citing works

AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation

Gómez, Ó., Holter, S., Yuan, J., & Bertini, E. (2021) · Visualization · doi:10.1109/vis49827.2021.9623271 · Cited by 26 · see citing works

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 · see citing works

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality

Lyu, Y., Lu, H., Lee, M. K., Schmitt, G., & Lim, B. Y. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3239909 · Cited by 24 · see citing works

Investigating the Intelligibility of Plural Counterfactual Examples for Non-Expert Users: an Explanation User Interface Proposition and User Study

Bove, C., Lesot, M., Tijus, C., & Detyniecki, M. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584082 · Cited by 23 · see citing works

SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration

Garcia-Zanabria, G., Gutierrez-Pachas, D. A., Cámara-Chávez, G., Poco, J., & Gomez-Nieto, E. (2022) · Applied Sciences · doi:10.3390/app12125785 · Cited by 21 · see citing works

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Wang, Z. J., Vaughan, J. W., Caruana, R., & Chau, D. H. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580816 · Cited by 20 · see citing works

Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans

Genovesi, S., Mönig, J. M., Schmitz, A., Poretschkin, M., Akila, M., Kahdan, M., Kleiner, R., Krieger, L., & Zimmermann, A. (2024) · AI and Ethics · doi:10.1007/s43681-023-00291-8 · Cited by 19 · see citing works

Decision Boundary Visualization for Counterfactual Reasoning

Sohns, J., Garth, C., & Leitte, H. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14650 · Cited by 17 · see citing works

XAI for learning: Narrowing down the digital divide between “new” and “old” experts

Simkute, A., Surana, A., Luger, E. A., Evans, M., & Jones, R. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3547522.3547678 · Cited by 17 · see citing works

ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection

Meng, L., van den Elzen, S., & Vilanova, A. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14525 · Cited by 16 · see citing works

Transparency, auditability, and explainability of machine learning models in credit scoring

Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2021.1922098 · Cited by 225 · see citing works

MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models

Guo, G., Deng, L., Tandon, A. A., Endert, A., & Kwon, B. C. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659011 · Cited by 13 · see citing works

A Multi-Criteria Decision-Making Approach for the Selection of Explainable AI Methods

Matejová, M., & Paralič, J. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7040158 · Cited by 15 · see citing works

Integration of Explainable AI and Blockchain for Secure Storage of Human Readable Justifications for Credit Risk Assessment

Walambe, R. A., Kolhatkar, A., Ojha, M., Kademani, A., Pandya, M., Kathote, S., & Kotecha, K. V. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-0404-1_5 · Cited by 12 · see citing works

AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance Visualization

Velumani, R. P., Xia, M., Han, J., Wang, C., Lau, A. K. H., & Qu, H. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511150 · Cited by 12 · see citing works

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 · see citing works

When in Doubt! Understanding the Role of Task Characteristics on Peer Decision-Making with AI Assistance

Salimzadeh, S., & Gadiraju, U. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3627043.3659567 · Cited by 11 · see citing works

An empirical study of counterfactual visualization to support visual causal inference

Wang, A. Z., Borland, D., & Gotz, D. (2024) · Information Visualization · doi:10.1177/14738716241229437 · Cited by 11 · see citing works

Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction

Samimi, R., Bhattacharya, A., Gosak, L., Štiglic, G., & Verbert, K. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736616 · Cited by 10 · see citing works

Generating Recommendations with Post-Hoc Explanations for Citizen Science

Zaken, D. B., Segal, A., Cavalier, D., Shani, G., & Gal, K. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3503252.3531290 · Cited by 10 · see citing works

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization

Mehdiyev, N., Majlatow, M., & Fettke, P. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10294-0 · Cited by 8 · see citing works

Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explanation Type and Presentation on Lay User Understanding

VanNostrand, P. M., Hofmann, D. M., Ma, L., & Rundensteiner, E. A. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658997 · Cited by 8 · see citing works

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 · see citing works

Understanding the User Perception and Experience of Interactive Algorithmic Recourse Customization

Koh, S., Kim, B. H., & Jo, S. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3674503 · Cited by 8 · see citing works

On the distributed software architecture of a data analysis workflow: A case study

Taşgetiren, N., Tigrak, U., Bozan, E., et al. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6522 · Cited by 7 · see citing works

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 7 · see citing works

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Wang, A. Z., Borland, D., & Gotz, D. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456369 · Cited by 7 · see citing works

Context sight

Yuan, J., & Bertini, E. (2022) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3546930.3547502 · Cited by 7 · see citing works

COIN: Counterfactual Image Generation for Visual Question Answering Interpretation

Boukhers, Z., Hartmann, T., & Jürjens, J. (2022) · Sensors · doi:10.3390/s22062245 · Cited by 7 · see citing works

Contextualising local explanations for non-expert users: an XAI pricing interface for insurancePreprint

Bove, C., Aigrain, J., Lesot, M., Tijus, C., & Detyniecki, M. (2021) · HAL · Cited by 6 · see citing works

Design of explanation user interfaces for interactive machine learning using the example of a knowledge graph-based approach to explainable process analysis

Füßl, A., & Nissen, V. (2025) · Requirements Engineering · doi:10.1007/s00766-025-00437-6 · Cited by 6 · see citing works

CSSE - An agnostic method of counterfactual, selected, and social explanations for classification models

Balbino, M. D. S., Zárate, L. E., & Nobre, C. N. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120373 · Cited by 6 · see citing works

Semantic Explanation of Interactive Dimensionality Reduction

Bian, Y., North, C. L., Krokos, E., & Joseph, S. (2021) · Visualization · doi:10.1109/vis49827.2021.9623322 · Cited by 6 · see citing works

Tooling for Developing Data-Driven Applications: Overview and Outlook

Weber, T., & Hußmann, H. (2022) · Mensch und Computer (MuC) · doi:10.1145/3543758.3543779 · Cited by 6 · see citing works

Multi-criteria Rank-based Aggregation for Explainable AI

Chatterjee, S., Colombo, E. R., & Raimundo, M. M. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228222 · Cited by 5 · see citing works

Exploring the impact of explainable AI and cognitive capabilities on users’ decisions

Cau, F. M., & Spano, L. D. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09438-0 · Cited by 9 · see citing works

MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data

Redelmeier, A., Jullum, M., Aas, K., & Løland, A. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01017-y · Cited by 5 · see citing works

Augmenting Decision Making via Interactive What-If AnalysisPreprint

Gathani, S., Hulsebos, M., Gale, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06160 · Cited by 5 · see citing works

Low-Cost Algorithmic Recourse for Users With Uncertain Cost FunctionsPreprint

Yadav, P., Hase, P., & Bansal, M. (2021) · arXiv.org · doi:10.48550/arxiv.2111.01235 · Cited by 14 · see citing works

A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review

Canha, D., Sylvain Kubler, A., Främling, K., & Fagherazzi, G. (2025) · ACM Computing Surveys · doi:10.1145/3737445 · Cited by 5 · see citing works

Counterfactual Explanations via Latent Space Projection and InterpolationPreprint

Barr, B., Harrington, M. R., Sharpe, S., & Bruss, C. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00890 · Cited by 4 · see citing works

Exploring Subjective Notions of Explainability through Counterfactual Visualization of Sentiment Analysis

Crisan, A., Butters, N., & Zoe (2024) · IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) · doi:10.1109/beliv64461.2024.00007 · Cited by 3 · see citing works

CounterCrime - Using Counterfactual Explanations to Explore Crime Reduction Scenarios

Raimundo, M. M., Garcia-Zanabria, G., Nonato, L. G., & Poco, J. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3586202 · Cited by 3 · see citing works

Interactive Visualization of Counterfactual Explanations for Tabular Data

Guyomard, V., Fessant, F., Guyet, T., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43430-3_25 · Cited by 2 · see citing works

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 · see citing works

CSDA-Vis: A (What-If-and-When) visual system for early dropout detection using counterfactual and survival analysis interactions

Garcia-Zanabria, G., Gutierrez-Pachas, D. A., Poco, J., & Gomez-Nieto, E. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104489 · Cited by 2 · see citing works

When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption

Erlei, A., Cau, F. M., Georgiev, R., Kumar, S. C., Bizer, K., & Gadiraju, U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791420 · Cited by 4 · see citing works

Dynamical counterfactual inference under time-series model for waterflooding oilfield

Wen, G., Min, C., Zhang, Q., & Liao, G. (2024) · Petroleum · doi:10.1016/j.petlm.2024.11.001 · Cited by 2 · see citing works

Explaining the Road Not TakenPreprint

Shen, H., & Huang, T.-H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14973 · Cited by 4 · see citing works

Visual Analytics for Fine‐grained Text Classification Models and Datasets

Battogtokh, M., Xing, Y., Davidescu, C., Abdul‐Rahman, A., Flucke, G., & Borgo, R. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15098 · Cited by 2 · see citing works

Causal Post-hoc XAI: Categorisation and Systematic Literature Review

Bjøru, A. R., Helge Langseth, A., Strümke, I., & Bach, K. (2026) · Machine Learning · doi:10.1007/s10994-026-07103-0 · Cited by 1 · see citing works

Explanation Hacking: The Perils of Algorithmic Recourse

Sullivan, E., & Kasirzadeh, A. (2025) · Synthese Library/Synthese library · doi:10.1007/978-3-032-03083-2_10 · Cited by 1 · see citing works

Explainable Artificial Intelligence for Industrial Cybersecurity: A Review of Methods, Operational Integration, and Research Challenges

Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171

How Can Credit Scoring Benefit from Machine Learning? SWOT Analysis

Bentounsi, O., & Lahmini, H. M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-64779-6_15

Enhancing Model Interpretability Through Interactive Visual Analysis and Counterfactual Explanation Methods

Lan, H., Pu, J., Xia, Y., He, Y., Zhang, Y., Huang, J., & Rao, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71315-6_4

Developing Long-Term Business Strategies by Leveraging Infeasible Recommendations of the Counterfactual Explanation Model

Ordibazar, A. H., Hussain, O. K., Chakrabortty, R. K., et al. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87769-8_16

Interactive Evolutionary Optimization of Visual Explainable AI through Gestalt Principles with Human Feedback

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UGCE: User-Guided Incremental Counterfactual Exploration

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Counterfactual Explanations with Graded Interactivity: Effects on Mental-Model Formation and Explanation SatisfactionPreprint

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Design and Evaluation of Unified Superimposed Visualization for a Large-Scale Heterogeneous Local Explanations

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ReVise: A Human-AI Interface for Incremental Algorithmic Recourse

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A Novel Objective Function for Counterfactual Explanations Using Conic Optimization

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Explaining Time Series Classifications Counterfactually: A Human-Centered Evaluation

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ViCE: Visual Counterfactual Explanations for Machine Learning ModelsPreprint

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AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation

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Enhancing Model Interpretability Through Interactive Visual Analysis and Counterfactual Explanation Methods

Lan, H., Pu, J., Xia, Y., He, Y., Zhang, Y., Huang, J., & Rao, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71315-6_4

Interactive Visualization of Counterfactual Explanations for Tabular Data

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DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

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