Research

Journal Publications

  • Goethals, S., Sörensen, K., & Martens, D. (2023). The privacy issue of counterfactual explanations: explanation linkage attacks. ACM Transactions on Intelligent Systems and Technology, 14(5), 1-24.
  • Goethals, S., Martens, D., & Calders, T. (2023). PreCoF: counterfactual explanations for fairness. Machine Learning, 1-32.
  • Goethals, S., Martens, D., & Evgeniou, T. (2022). The non-linear nature of the cost of comprehensibility. Journal of Big Data, 9(1), 1-23.
  • Vermeire, T., Brughmans, D., Goethals, S., de Oliveira, R. M. B., & Martens, D. (2022). Explainable image classification with evidence counterfactual. Pattern Analysis and Applications, 25(2), 315-335.

Conference Publications

  • Goethals, S., Martens, D., & Evgeniou, T. (forthcoming). Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. Joint European Conference on Machine Learning and Knowledge Discovery.
  • Goethals, S., Martens, D., & Calders, T. (2023). Explainability methods to detect and measure discrimination in machine learning models. In European Workshop on Algorthmic Fairness. CEUR workshop proceedings (Vol. 3442, pp. 1-5).
  • Mazzine, R., Goethals, S., Brughmans, D., & Martens, D. (2021). Counterfactual explanations for employment services. In International workshop on Fair, Effective And Sustainable Talent management using data science (pp. 1-7).

Preprints

  • Goethals, S., Delaney, E., Mittelstadt, B. & Russell, C. (2024). Resource-constrained fairness.
  • Goethals, S. & Calders, T. (2024). Reranking individuals: The effect of fair classification within-groups.
  • Rhue, L., Goethals, S., & Sundararajan, A. (2024). Evaluating LLMs for Gender Disparities in Notable Persons.
  • Goethals, S., Matz, S., Provost, F., Martens, D. & Ramon, Y. (2023). Implications of Cloaking Digital Footprints for Privacy and Personalization
  • Greene, T., Goethals, S., Martens, D. & Shmueli, G. (2023). Monetizing Explainable AI: A Double-edged Sword.