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Interpretable machine learning and explainable artificial intelligence

Author

Listed:
  • Kazim Topuz

    (Collins College of Business, The University of Tulsa)

  • Akhilesh Bajaj

    (Collins College of Business, The University of Tulsa)

  • Kristof Coussement

    (Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management)

  • Timothy L. Urban

    (Collins College of Business, The University of Tulsa)

Abstract

No abstract is available for this item.

Suggested Citation

  • Kazim Topuz & Akhilesh Bajaj & Kristof Coussement & Timothy L. Urban, 2025. "Interpretable machine learning and explainable artificial intelligence," Annals of Operations Research, Springer, vol. 347(2), pages 775-782, April.
    • Handle: RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-025-06577-w
    DOI: 10.1007/s10479-025-06577-w
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    References listed on IDEAS

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    1. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    2. Nolan M. Talaei & Asil Oztekin & Luvai Motiwalla, 2025. "From rants to raves: unraveling movie critics’ reviews with explainable artificial intelligence," Annals of Operations Research, Springer, vol. 347(2), pages 937-957, April.
    3. Xiaoqing Ye & Dun Liu & Tianrui Li & Wenjie Li, 2025. "Heterogeneous business network based interpretable competitive firm identification: a graph neural network method," Annals of Operations Research, Springer, vol. 347(2), pages 1133-1161, April.
    4. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    5. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    6. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    7. Edward Elson Kosasih & Alexandra Brintrup, 2022. "A machine learning approach for predicting hidden links in supply chain with graph neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5380-5393, September.
    8. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    9. Sumeyra Sahbaz & Kazim Topuz & Seth J. Schwartz & Pablo Montero-Zamora, 2025. "Understanding cultural stress and mental health among Latinos in the us: probabilistic omnidirectional inference model," Annals of Operations Research, Springer, vol. 346(3), pages 2461-2481, March.
    10. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    11. Stefan Lessmann & Kristof Coussement & Koen W. de Bock & Johannes Haupt, 2019. "Targeting customers for profit: An ensemble learning framework to support marketing decision-making," Post-Print hal-02275955, HAL.
    12. Kozodoi, Nikita & Jacob, Johannes & Lessmann, Stefan, 2022. "Fairness in credit scoring: Assessment, implementation and profit implications," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1083-1094.
    13. Kristof Coussement & Stefan Lessmann & Geert Verstraeten, 2017. "A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry," Post-Print hal-01745261, HAL.
    14. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    15. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    16. Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021. "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers 2103.01907, arXiv.org, revised Jun 2022.
    17. Kristof Coussement & Paul Harrigan & Dries Benoit, 2015. "Improving direct mail targeting through customer response modeling," Post-Print hal-02990995, HAL.
    18. Enes Eryarsoy & Kazim Topuz & Cenk Demiroglu, 2024. "Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach," Annals of Operations Research, Springer, vol. 335(2), pages 761-795, April.
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