A peek into the black box: exploring classifiers by randomization
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Blinding audit approaches explore how partially blinding features (sensitive or otherwise) affect model performance.
“Speciically, such approaches explore how partially blinding features (sensitive or otherwise) afect model performance.”
From Fairness in machine learning: A survey (2024) · cites this paper as [143]
Blinding (or partial blinding) has been used as a fairness audit mechanism.
“Blinding (or partial blinding) has also been used as a fairness audit mechanism [2,82,143].”
From Fairness in machine learning: A survey (2024) · cites this paper as [143]
Causal methods can provide visual descriptions of (un)fairness in the dataset.
“Causal methods can provide visual descriptions of (un)fairness in the dataset (see [143,173,182,183]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [143]
Causal methods are specifically well suited to providing transparency with respect to how classification decisions were made.
“Thus, causal methods are speciically well suited to identifying 1) proxies of sensitive variables as discussed in subsection 2.1; 2) which subgroups are most (un)fairly treated and diferentiate the types of bias exhibited [118]; and 3) provide transparency with respect to how (classiication) decisions were made [143].”
From Fairness in machine learning: A survey (2024) · cites this paper as [143]
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
Bias in random forest variable importance measures: Illustrations, sources and a solution
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-25 · Cited by 3,755 · see citing works
Conditional variable importance for random forests
Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,299 · see citing works
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Domingos, P., & Pazzani, M. J. (1997) · Machine Learning · doi:10.1023/a:1007413511361 · Cited by 3,115 · see citing works
Clark, P. A., & Niblett, T. (1989) · Machine Learning · doi:10.1023/a:1022641700528 · Cited by 2,267 · see citing works
Survey and critique of techniques for extracting rules from trained artificial neural networks
Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225 · see citing works
Ishibuchi, H., & Nojima, Y. (2006) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2006.01.004 · Cited by 389 · see citing works
Open-source machine learning: R meets Weka
Hornik, K., Buchta, C., & Zeileis, A. (2008) · Computational Statistics · doi:10.1007/s00180-008-0119-7 · Cited by 388 · see citing works
Permutation Tests for Studying Classifier Performance
Ojala, M., & Garriga, G. C. (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859913 · Cited by 343 · see citing works
An AUC-based permutation variable importance measure for random forests
Janitza, S., Strobl, C., & Boulesteix, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-119 · Cited by 265 · see citing works
Searching for interacting features
Zhao, Z., & Liu, H. (2007) · International Joint Conference on Artificial Intelligence · Cited by 235 · see citing works
Understanding the Crucial Role of Attribute Interaction in Data Mining
Freitas, A. A. (2001) · Artificial Intelligence Review · doi:10.1023/a:1011996210207 · Cited by 157 · see citing works
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Bie, T. D. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0209-3 · Cited by 149 · see citing works
Searching for interacting features in subset selection1
Zhao, Z., & Liu, H. (2009) · Intelligent Data Analysis · doi:10.3233/ida-2009-0364 · Cited by 107 · see citing works
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
Pulkkinen, P., & Koivisto, H. J. (2007) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2007.10.004 · Cited by 100 · see citing works
An information theoretic framework for data mining
Bie, T. D. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020497 · Cited by 98 · see citing works
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data MiningPreprint
Hanhijärvi, S., Ojala, M., Vuokko, N., Puolamäki, K., Tatti, N., & Mannila, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09467 · Cited by 69 · see citing works
Self-citationAttribute Interactions in Medical Data Analysis
Jakulin, A., Bratko, I., Smrke, D., Demšar, J., & Zupan, B. (2003) · Conference on Artificial Intelligence in Medicine in Europe · doi:10.1007/978-3-540-39907-0_32 · Cited by 61 · see citing works
Relating Amino Acid Sequence to Phenotype: Analysis of Peptide‐Binding Data
Segal, M. R., Cummings, M. P., & Hubbard, A. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.00632.x · Cited by 58 · see citing works
Tell me something I don't know
Hanhijärvi, S., Ojala, M., Vuokko, N., Puolamäki, K., Tatti, N., & Mannila, H. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557065 · Cited by 53 · see citing works
Self-citationPlate, T. A. (1999) · Behaviormetrika · doi:10.2333/bhmk.26.29 · Cited by 49 · see citing works
A statistical significance testing approach to mining the most informative set of patterns
Lijffijt, J., Papapetrou, P., & Puolamäki, K. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0298-2 · Cited by 40 · see citing works
Self-citationMining of Attribute Interactions Using Information Theoretic Metrics
Chanda, P., Cho, Y., Zhang, A., & Ramanathan, M. K. (2009) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2009.51 · Cited by 28 · see citing works
Zacarias, O. P., & Boström, H. (2013) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2013.6761181 · Cited by 15 · see citing works
Self-citationA Framework for Evaluating the Smoothness of Data-Mining Results
Misra, G., Golshan, B., & Terzi, E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33486-3_42 · Cited by 4 · see citing works
Explaining Interval Sequences by Randomization
Henelius, A., Korpela, J., & Puolamäki, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40988-2_22 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043 · see citing works
A Survey on the Explainability of Supervised Machine Learning
Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008 · see citing works
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.06.002 · Cited by 482 · see citing works
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegger, C., Smith, B., & Venkatasubramanian, S. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-017-1116-3 · Cited by 280 · 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
Meaningful Explanations of Black Box AI Decision Systems
Pedreschi, D., Giannotti, F., Guidotti, R., Monreale, A., Ruggieri, S., & Turini, F. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019780 · Cited by 233 · see citing works
Counterfactual Explanations for Machine Learning: A Review.Preprint
Verma, S., Dickerson, J. P., & Hines, K. (2020) · arXiv (Cornell University) · Cited by 223 · see citing works
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102941 · Cited by 170 · see citing works
Classification of Explainable Artificial Intelligence Methods through Their Output Formats
Vilone, G., & Longo, L. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3030032 · Cited by 157 · see citing works
Explaining deep neural networks: A survey on the global interpretation methods
Saleem, R., Yuan, B., Kurugöllü, F., Anjum, A., & Liu, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.129 · Cited by 150 · see citing works
Chomutare, T., Tejedor, M., Svenning, T. O., Marco-Ruiz, L., Tayefi, M., Lind, K., Godtliebsen, F., Moen, A., Ismail, L., Makhlysheva, A., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316359 · Cited by 156 · see citing works
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works
“Another technique of model explanation is called Golden-Eye [111], which is based on data randomization to identify groups of features whose interactions have an impact on the prediction.”
MethodFairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
Tan, S., Caruana, R., Hooker, G., & Lou, Y. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278725 · Cited by 127 · see citing works
Fairness in Recommendation: Foundations, Methods, and Applications
Li, Y., Chen, H., Xu, S., Ge, Y., Tan, J., Liu, S., & Zhang, Y. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3610302 · Cited by 147 · see citing works
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98 · see citing works
A review of explainable artificial intelligence in smart manufacturing
Madathil, A. P., Luo, X., Liu, Q., Walker, C. T., Madarkar, R., & Qin, Y. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2513574 · Cited by 111 · see citing works
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Turner, R. (2016) · Machine learning for signal processing ... · doi:10.1109/mlsp.2016.7738872 · Cited by 77 · see citing works
Charte, D., Charte, F., del Jesús, M. J., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.057 · Cited by 74 · see citing works
Exploring Evaluation Methods for Interpretable Machine Learning: A Survey
Alangari, N., Menaï, M. E. B., Mathkour, H. I., & Almosallam, I. A. (2023) · Information · doi:10.3390/info14080469 · Cited by 65 · see citing works
Mitigating Bias in Algorithmic Systems—A Fish-eye View
Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2022) · ACM Computing Surveys · doi:10.1145/3527152 · Cited by 54 · see citing works
Principles and Practice of Explainable Machine Learning
Belle, V., & Papantonis, I. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.688969 · Cited by 99 · see citing works
Data Science for Economics and Finance
Consoli, S., Recupero, D. R., & Saisana, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4 · Cited by 45 · see citing works
Solanke, A. A. (2022) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2022.301403 · Cited by 52 · see citing works
Declarative Aspects in Explicative Data Mining for Computational Sensemaking
Atzmueller, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00801-7_7 · Cited by 36 · see citing works
EVE: explainable vector based embedding technique using Wikipedia
Qureshi, M. A., & Greene, D. (2018) · Journal of Intelligent Information Systems · doi:10.1007/s10844-018-0511-x · Cited by 34 · see citing works
Boxer: Interactive Comparison of Classifier Results
Gleicher, M. L., Barve, A., Yu, X., & Heimerl, F. (2020) · Computer Graphics Forum · doi:10.1111/cgf.13972 · Cited by 34 · see citing works
A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?
Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 32 · see citing works
FCE: Feedback Based Counterfactual Explanations for Explainable AI
Suffian, M., Graziani, P., Alonso, J. M., & Bogliolo, A. (2022) · IEEE Access · doi:10.1109/access.2022.3189432 · Cited by 32 · see citing works
The Past, Present, and Prospective Future of XAI: A Comprehensive Review
Islam, M. U., Mottalib, M. M., Hassan, M., Alam, Z. I., Zobaed, S., & Rabby, M. F. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-96630-0_1 · Cited by 30 · see citing works
Wang, B., Pei, W., Xue, B., & Zhang, M. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3225591 · Cited by 31 · 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
Making machine learning useable by revealing internal states update - a transparent approach
Zhou, J. J., Khawaja, M. A., Li, Z., Sun, J., Wang, Y., & Chen, F. (2016) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2016.080214 · Cited by 27 · see citing works
Ouyang, Y., Wu, Y., Wang, H., Zhang, C., Cheng, F., Jiang, C., Jin, L., Cao, Y., & Li, Q. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3326929 · Cited by 27 · see citing works
Improving the Quality of Explanations with Local Embedding Perturbations
Jia, Y., Bailey, J., Ramamohanarao, K., Leckie, C. A., & Houle, M. E. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330930 · Cited by 25 · see citing works
Coalitional Strategies for Efficient Individual Prediction Explanation
Ferrettini, G., Escriva, E., Aligon, J., Excoffier, J.-B., & Soulé-Dupuy, C. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10141-9 · Cited by 25 · see citing works
Understanding Where Your Classifier Does (Not) Work -- The SCaPE Model Class for EMM
Duivesteijn, W., & Thaele, J. (2014) · Proceedings · doi:10.1109/icdm.2014.10 · Cited by 25 · see citing works
A model-agnostic, network theory-based framework for supporting XAI on classifiers
Bonifazi, G., Cauteruccio, F., Corradini, E., Marchetti, M., Giorgio Terracina, B., Domenico Ursino, A., & Virgili, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122588 · Cited by 23 · see citing works
SLISEMAP: supervised dimensionality reduction through local explanations
Björklund, A., Mäkelä, J., & Puolamäki, K. (2022) · Machine Learning · doi:10.1007/s10994-022-06261-1 · Cited by 21 · see citing works
Self-citationFeature Importance Explanations for Temporal Black-Box Models
Sood, A., & Craven, M. (2021) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20810 · Cited by 27 · see citing works
Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI
Lukyanenko, R., Castellanos, A., Storey, V. C., Castillo, A., Tremblay, M. C., & Parsons, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65847-2_3 · Cited by 19 · see citing works
Li, B., Yang, Y., Liu, D., Zhang, Y., Zhou, A., & Yao, X. (2024) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2024.101610 · Cited by 18 · see citing works
Exploiting patterns to explain individual predictions
Jia, Y., Bailey, J., Ramamohanarao, K., Leckie, C. A., & Ma, X. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-019-01368-9 · Cited by 18 · see citing works
An Information-Theoretic Quantification of Discrimination with Exempt Features
Dutta, S., Venkatesh, P., Mardziel, P., Datta, A., & Grover, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5794 · Cited by 18 · see citing works
Disentangling Influence: Using Disentangled Representations to Audit Model PredictionsPreprint
Marx, C. T., Phillips, R. L., Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08652 · Cited by 16 · see citing works
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Nguyen, V. B., Schlötterer, J., & Seifert, C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_4 · Cited by 14 · see citing works
Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP
Onyekpe, U., Lu, Y., Apostolopoulou, E., Palade, V., Eyo, E., & Kanarachos, S. A. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_8 · Cited by 13 · see citing works
Why Should I Trust This Item? Explaining the Recommendations of any Model
Lonjarret, C., Robardet, C., Plantevit, M., Auburtin, R., & Atzmueller, M. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00067 · Cited by 13 · see citing works
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
Dutta, S., Venkatesh, P., Mardziel, P., Datta, A., & Grover, P. (2021) · IEEE Transactions on Information Theory · doi:10.1109/tit.2021.3103206 · Cited by 11 · see citing works
GoldenEye++: A Closer Look into the Black Box
Henelius, A., Puolamäki, K., Karlsson, I., Zhao, J., Asker, L., Boström, H., & Papapetrou, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17091-6_5 · Cited by 11 · see citing works
Self-citationMachine Guides, Human Supervises: Interactive Learning with Global ExplanationsPreprint
Popordanoska, T., Kumar, M., & Teso, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09723 · Cited by 13 · see citing works
Methods for explaining Top-N recommendations through subgroup discovery
Iferroudjene, M., Lonjarret, C., Robardet, C., Plantevit, M., & Atzmueller, M. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00897-2 · Cited by 10 · see citing works
Capturing the form of feature interactions in black-box models
Zhang, H., Zhang, X., Zhang, T., & Zhu, J. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103373 · Cited by 9 · see citing works
Buckmann, M., Joseph, A. C., & Robertson, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4_3 · Cited by 9 · see citing works
Sparse Robust Regression for Explaining Classifiers
Björklund, A., Henelius, A., Oikarinen, E., Kallonen, K. T. S., & Puolamäki, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33778-0_27 · Cited by 9 · see citing works
Self-citationExplaining any black box model using real data
Björklund, A., Henelius, A., Oikarinen, E., Kallonen, K. T. S., & Puolamäki, K. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1143904 · Cited by 8 · see citing works
Self-citationNasir, M., Summerfield, N. S., Simsek, S., & Oztekin, A. (2024) · Decision Sciences · doi:10.1111/deci.12642 · Cited by 8 · see citing works
Hess, S., Duivesteijn, W., & Mocanu, D. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01987 · Cited by 8 · see citing works
Local interpretation of supervised learning models based on high dimensional model representation
Zhang, X., Ling, W., & Li, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2021.01.079 · Cited by 7 · see citing works
Genga, L., Allodi, L., & Zannone, N. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2010011 · Cited by 7 · see citing works
Are you sure? Prediction revision in automated decision‐making
Burkart, N., Robert, S., & Huber, M. F. (2020) · Expert Systems · doi:10.1111/exsy.12577 · Cited by 7 · see citing works
A Surrogate Approach to Explainable AI for Predictive Maintenance: Techniques and Applications
Rakesh, P., Shruti, J. R., Thippeswamy, I., Nithya, B., & Dheeraj, V. (2024) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/i4c62240.2024.10748524 · Cited by 6 · see citing works
Zhang, X., Wang, Y., & Li, Z. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02255-z · Cited by 6 · see citing works
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Alangari, N., Menaï, M. E. B., Mathkour, H. I., & Almosallam, I. A. (2023) · Information · doi:10.3390/info14030164 · Cited by 6 · see citing works
Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data
Qi, W., & Chelmis, C. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671677 · Cited by 6 · see citing works
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Wang, X., He, J., Jin, Z., Yang, M., Wang, Y., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114794 · Cited by 6 · see citing works
A survey of computational methods for fossil data analysis
Žliobaitė, I., Puolamäki, K., Eronen, J. T., & Fortelius, M. (2017) · Evolutionary ecology research · Cited by 5 · see citing works
Self-citationCounterfactual Explainer for Deep Reinforcement Learning Models using Policy Distillation
Samadi, A., Koufos, K., Debattista, K., & Dianati, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3709146 · Cited by 8 · see citing works
Arun, P. V., & Karnieli, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07019-5 · Cited by 5 · see citing works
Explaining Single Predictions: A Faster Method
Ferrettini, G., Aligon, J., & Soulé-Dupuy, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38919-2_26 · Cited by 5 · see citing works
Improving on Coalitional Prediction Explanation
Ferrettini, G., Aligon, J., & Soulé-Dupuy, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-54832-2_11 · Cited by 5 · see citing works
Revealing the structure of prediction models through feature interaction detection
Zhang, X., Zhang, H., Zhu, J., & Li, Z. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107737 · Cited by 4 · see citing works
Canha, D., Sylvain Kubler, A., Främling, K., & Fagherazzi, G. (2025) · ACM Computing Surveys · doi:10.1145/3737445 · Cited by 5 · see citing works
How else can we define Information Flow in Neural Circuits?
Venkatesh, P., Dutta, S., & Grover, P. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174028 · Cited by 4 · see citing works
Learning fair models and representations
Oneto, L. (2020) · Intelligenza Artificiale · doi:10.3233/ia-190034 · Cited by 4 · see citing works
Conceptual Modeling Interacts with Machine Learning – A Systematic Literature Review
Zaidi, M. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87013-3_39 · Cited by 3 · see citing works
Identifying Factors for the Effectiveness of Treatment of Heart Failure: A Registry Study
Asker, L., Boström, H., Papapetrou, P., & Persson, H. (2016) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2016.50 · Cited by 3 · see citing works
Self-citationAmortized Generation of Sequential Counterfactual Explanations for Black-box Models.Preprint
Verma, S., Hines, K., & Dickerson, J. P. (2021) · arXiv (Cornell University) · Cited by 3 · see citing works
Real-Time and Trustworthy Classification of IoT Traffic Using Lightweight Deep Learning
Sivanathan, A., Mishra, D., Ruj, S., Fernandes, N., Sheng, Q. Z., Tran, M., Luo, B., Coscia, D., Batista, G., & Gharakheili, H. H. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3628913 · Cited by 2 · see citing works
How Different Explanations Impact Trust Calibration: The Case of Clinical Decision Support SystemsPreprint
Naiseh, M., Al‐Thani, D., Jiang, N., & Ali, R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4098528 · Cited by 2 · see citing works
A Systematic Review on Human Roles, Solutions, and Methodological Approaches to Address Bias in AI
Hashky, A., & Ragan, E. D. (2026) · ACM Computing Surveys · doi:10.1145/3793667 · Cited by 1 · see citing works
Understanding Where Your Classifier Does (Not) Work
Duivesteijn, W., & Thaele, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23461-8_24 · Cited by 1 · see citing works
Data Mining with Algorithmic Transparency
Zhou, Y., Alufaisan, Y., & Kantarcıoğlu, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93034-3_11 · Cited by 1 · see citing works
Mitigating Bias in Algorithmic Systems: A Fish-Eye View of Problems and Solutions Across Domains.Preprint
Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2021) · arXiv (Cornell University) · Cited by 1 · see citing works
Torgo, L., Azevedo, P. J., & Areosa, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08216 · Cited by 1 · see citing works
Learning from Swedish Healthcare Data
Asker, L., Papapetrou, P., & Boström, H. (2016) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2910674.2935859
Self-citationAccelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine LearningPreprint
Li, B., Yang, Y., Liu, D., Zhang, Y., Zhou, A., & Yao, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4699560
Sequential recommendation and explanations
Lonjarret, C. (2021) · theses.fr (ABES) · doi:10.70675/f4c49a1cz50c5z4bb5z8e1fz80c6c8791a58
- Authors
- Henelius, A., Puolamäki, K., Boström, H., Asker, L., & Papapetrou, P.
- Affiliations
- Finnish Institute of Occupational Health (FI)
- Stockholm University (SE)
- Corresponding author
- Henelius, Andreas
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2014-07-23
- Volume
- 28
- Issue
- 5-6
- Pages
- 1503-1529
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2090026354
- Cited by
- 98
- References
- 35
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 8 · 2023: 11 · 2022: 17 · 2021: 21 · 2020: 15 · 2019: 8 · 2018: 5 · 2017: 1 · 2016: 5 · 2015: 2 · 2014: 1
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- Funders
- Tekes
- Stockholms Universitet
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- classifier interpretability, feature importance, attribute interaction, randomization, black-box classifiers, classifier comparison, feature selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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