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Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice1

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Interpersonal Conflict and Its Management in Information System Development1

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Uncovering the Intellectual Core of the Information Systems Discipline1

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Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

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Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection

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The Impact of Explanation Facilities on User Acceptance of Expert Systems Advice

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50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science

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Explaining Classifications For Individual Instances

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Decisional Guidance for Computer-Based Decision Support1

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OPUS: An Efficient Admissible Algorithm for Unordered Search

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Document-Word Co-regularization for Semi-supervised Sentiment Analysis

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The Differential Use and Effect of Knowledge-Based System Explanations in Novice and Expert Judgment Decisions1

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Providing Decisional Guidance for Multicriteria Decision Making in Groups

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Explaining instance classifications with interactions of subsets of feature values

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How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations

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The effects of structural characteristics of explanations on use of a DSS

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Why label when you can search?

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On Chomsky and the Two Cultures of Statistical Learning

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Design principles of massive, robust prediction systems

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Audience Selection for On-Line Brand Advertising: Privacy-Friendly Social Network TargetingPreprint

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

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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A Survey on the Explainability of Supervised Machine Learning

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A data-driven approach to predict the success of bank telemarketing

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Explaining Explanations in AI

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

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Counterfactual explanations and how to find them: literature review and benchmarking

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Actionable Recourse in Linear Classification

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GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

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Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

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Artificial intelligence in E-Commerce: a bibliometric study and literature review

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

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Inherent Tradeoffs in the Fair Determination of Risk Scores

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Getting value from Business Intelligence systems: A review and research agenda

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

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Utilizing big data analytics for information systems research: challenges, promises and guidelines

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Benchmarking and survey of explanation methods for black box models

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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

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Human Digital Twin for Fitness Management

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Beware explanations from AI in health care

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The hidden assumptions behind counterfactual explanations and principal reasons

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Designing Transparency for Effective Human-AI Collaboration

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Human factors in model interpretability: Industry practices, challenges, and needs

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Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences

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Counterfactual Explanations for Machine Learning: A Review.Preprint

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Text mining for information systems researchers: An annotated topic modeling tutorial

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Comprehensible Predictive Models for Business Processes1

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Comparing Automatic and Human Evaluation of Local Explanations for Text Classification

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A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

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Social network analysis for customer churn prediction

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

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Mathematical optimization in classification and regression trees

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Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

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Comparison-Based Inverse Classification for Interpretability in Machine Learning

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Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics1

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Explainable Artificial Intelligence for Tabular Data: A Survey

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Algorithm-assisted decision-making in the public sector: framing the issues using administrative law rules governing discretionary power

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Post-hoc explanation of black-box classifiers using confident itemsets

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Explainability as a Non-Functional Requirement

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Evaluating and understanding text-based stock price prediction models

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Survey on Explainable AI: Techniques, challenges and open issues

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Explainable artificial intelligence in information systems: A review of the status quo and future research directions

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Counterfactual Explanations for Multivariate Time Series

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

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Explainable image classification with evidence counterfactual

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Digital Responsibility

Trier, M., Kundisch, D., Beverungen, D., Müller, O., Schryen, G., Mirbabaie, M., & Trang, S. T.-N. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00822-x · Cited by 97 · see citing works

Explainable Fuzzy Systems

Alonso, J. M., Castiello, C., Magdalena, L., & Mencar, C. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-71098-9 · Cited by 87 · see citing works

PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

Ghazimatin, A., Balalau, O., Saha Roy, R., & Weikum, G. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371824 · Cited by 88 · see citing works

Acceptance of text-mining systems: The signaling role of information quality

Demoulin, N. T. M., & Coussement, K. (2018) · Information & Management · doi:10.1016/j.im.2018.10.006 · Cited by 86 · see citing works

How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence

Kim, D., Song, Y., Kim, S., Lee, S., Wu, Y., Shin, J., & Lee, D. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122343 · Cited by 89 · see citing works

Value-added tax fraud detection with scalable anomaly detection techniques

Vanhoeyveld, J., Martens, D., & Peeters, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105895 · Cited by 91 · see citing works

On cognitive preferences and the plausibility of rule-based models

Fürnkranz, J., Kliegr, T., & Paulheim, H. (2019) · Machine Learning · doi:10.1007/s10994-019-05856-5 · Cited by 88 · see citing works

“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 81 · see citing works

Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals

Wang, Z., & Culotta, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17651 · Cited by 81 · 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

Towards a data science toolbox for industrial analytics applications

Flath, C. M., & Stein, N. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.09.003 · Cited by 74 · see citing works

Comprehensible software fault and effort prediction: A data mining approach

Moeyersoms, J., de Fortuny, E. J., Dejaeger, K., Baesens, B., & Martens, D. (2014) · Journal of Systems and Software · doi:10.1016/j.jss.2014.10.032 · Cited by 71 · 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

Dance Hit Song Prediction

Herremans, D., Martens, D., & Sörensen, K. (2014) · Journal of New Music Research · doi:10.1080/09298215.2014.881888 · Cited by 69 · see citing works

The use of big data in corporate communication

Wiencierz, C., & Röttger, U. (2017) · Corporate Communications An International Journal · doi:10.1108/ccij-02-2016-0015 · Cited by 68 · see citing works

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Meske, C., & Bunde, E. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10234-5 · Cited by 67 · see citing works

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 76 · see citing works

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67 · see citing works

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 · see citing works

Counterfactual Explanations for Neural Recommenders

Tran, K. H., Ghazimatin, A., & Roy, R. S. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463005 · Cited by 65 · see citing works

Active Learning-Based Pedagogical Rule Extraction

de Fortuny, E. J., & Martens, D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2389037 · Cited by 63 · see citing works

Identifying Spurious Correlations for Robust Text Classification

Wang, Z., & Culotta, A. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.308 · Cited by 64 · see citing works

Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling

Bock, K. W. D., & Caigny, A. D. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113523 · Cited by 54 · see citing works

What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

Cnudde, S. D., Moeyersoms, J., Stankova, M., Tobback, E., Javaly, V., & Martens, D. (2018) · Journal of the Operational Research Society · doi:10.1080/01605682.2018.1434402 · Cited by 69 · see citing works

sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

Yang, Y., Zhang, K., & Fan, Y. (2022) · Information Systems Research · doi:10.1287/isre.2022.1124 · Cited by 48 · see citing works

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

Fernández-Loría, C., Provost, F. J., & Han, X. (2022) · MIS Quarterly · doi:10.25300/misq/2022/16749 · Cited by 115 · 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

ChatrEx: Designing Explainable Chatbot Interfaces for Enhancing Usefulness, Transparency, and Trust

Khurana, A., Harjandi, P. A., & Chilana, P. K. (2021) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc51201.2021.9576440 · Cited by 49 · see citing works

Corporate residence fraud detection

de Fortuny, E. J., Stankova, M., Moeyersoms, J., Minnaert, B., Provost, F. J., & Martens, D. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623333 · Cited by 45 · see citing works

Imbalanced classification in sparse and large behaviour datasets

Vanhoeyveld, J., & Martens, D. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0517-y · Cited by 45 · see citing works

On Fairness and Calibration

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603207 · Cited by 49 · see citing works

Machine learning in information systems - a bibliographic review and open research issues

Abdel-Karim, B. M., Pfeuffer, N., & Hinz, O. F. (2021) · Electronic Markets · doi:10.1007/s12525-021-00459-2 · Cited by 50 · see citing works

A Framework for Explainable Text Classification in Legal Document Review

Mahoney, C. J., Zhang, J., Huber-Fliflet, N., Gronvall, P., & Zhao, H. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005659 · Cited by 41 · see citing works

Predictive Multiplicity in ClassificationPreprint

Marx, C. T., Calmon, F. P., & Ustun, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06677 · Cited by 41 · see citing works

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Ramon, Y., Farrokhnia, R. A., Matz, S., & Martens, D. (2021) · Information · doi:10.3390/info12120518 · Cited by 40 · see citing works

The Robustness of Counterfactual Explanations Over Time

Ferrario, A., & Loi, M. (2022) · IEEE Access · doi:10.1109/access.2022.3196917 · Cited by 38 · see citing works

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

Chen, D., Fraiberger, S. P., Moakler, R., & Provost, F. J. (2017) · Big Data · doi:10.1089/big.2017.0074 · Cited by 37 · see citing works

Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding

Chhatwal, R., Gronvall, P., Huber-Fliflet, N., Keeling, R., Zhang, J., & Zhao, H. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622073 · Cited by 37 · see citing works

How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card

Kühl, N., Hirt, R., Baier, L., Schmitz, B., & Satzger, G. (2021) · Communications of the Association for Information Systems · doi:10.17705/1cais.04845 · Cited by 37 · see citing works

AI-Assisted and Explainable Hate Speech Detection for Social Media Moderators – A Design Science Approach

Bunde, E. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.154 · Cited by 36 · see citing works

Tell me a story! Narrative-driven XAI with Large Language Models

Martens, D., Hinns, J., Dams, C., Vergouwen, M., & Evgeniou, T. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114402 · Cited by 43 · see citing works

Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers

Stepin, I., Alonso, J. M., Catalá, A., & Pereira-Fariña, M. (2020) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177629 · Cited by 35 · see citing works

Design Science Research Modes in Human-Computer Interaction Projects

Adam, M. T. P., Gregor, S. D., Hevner, A. R., & Morana, S. (2021) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00139 · Cited by 35 · see citing works

Explaining clusterings of process instances

Koninck, P. D., Weerdt, J. D., & Broucke, S. V. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0488-4 · Cited by 34 · see citing works

Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms

Carreira-Perpiñán, M. Á., & Hada, S. S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16851 · Cited by 34 · see citing works

Customs fraud detection

Vanhoeyveld, J., Martens, D., & Peeters, B. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00852-w · Cited by 32 · see citing works

PreCoF: counterfactual explanations for fairness

Goethals, S., Martens, D., & Calders, T. (2023) · Machine Learning · doi:10.1007/s10994-023-06319-8 · Cited by 31 · see citing works

Clustering categories in support vector machines

Carrizosa, E., Nogales-Gómez, A., & Morales, D. R. (2016) · Omega · doi:10.1016/j.omega.2016.01.008 · Cited by 31 · see citing works

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 34 · see citing works

Interpretability and Transparency in Artificial Intelligence

Mittelstadt, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198857815.013.20 · Cited by 30 · see citing works

Telecom traffic pumping analytics via explainable data science

Irarrázaval, M. E., Maldonado, S., Pérez, J., & Vairetti, C. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113559 · Cited by 29 · see citing works

Mathematical optimization modelling for group counterfactual explanations

Carrizosa, E., Ramírez-Ayerbe, J., & Morales, D. R. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.01.002 · Cited by 29 · see citing works

Unjustified Classification Regions and Counterfactual Explanations in Machine Learning

Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., & Detyniecki, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-46147-8_3 · Cited by 28 · see citing works

Disagreement amongst counterfactual explanations: how transparency can be misleading

Brughmans, D., Melis, L., & Martens, D. (2024) · Top · doi:10.1007/s11750-024-00670-2 · Cited by 30 · 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

Explaining Documents' ClassificationsPreprint

Martens, D., & Provost, F. J. (2011) · SSRN Electronic Journal · Cited by 6 · see citing works

Working paper CeDER-11-01 Explaining Documents ’ Classifications

Martens, D., & Provost, F. J. (2013)

PERFEX: Classifier Performance Explanations for Trustworthy AI Systems

Walraven, E., Adhikari, A., & Veenman, C. J. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_9 · Cited by 2 · see citing works

Towards Learning with Feature-Based Explanations for Document Classification

Sharma, M., & Bilgic, M. (2016) · International Joint Conference on Artificial Intelligence