Goethals, S., Matz, S., Provost, F., Martens, D. & Ramon, Y. (2025). The Impact of Cloaking Digital Footprints on User Privacy and Personalization. Big Data.
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.
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.
Goethals, S., Sedoc, J., & Provost, F. (2025, June). What If the Prompt Were Different? Counterfactual Explanations for the Characteristics of Generative Outputs. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (pp. 237-242).
Goethals, S., Luther, J., & Matz, S. (2025, June). Words reveal wants: How well can simple LLM-based AI agents replicate people’s choices based on their social media posts. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (pp. 126-131).
Goethals, S. & Rhue, L. (2025). One world, one opinion? The superstar effect in LLM responses. In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP.
Goethals, S., Martens, D., & Evgeniou, T. (2023). 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).