Publications

• C. Mougan, D. S. Nielsen. Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap. The 37th AAAI Conference on Artificial Intelligence (AAAI 2023).
• S. Ruggieri, J. M. Alvarez, A. Pugnana, L. State, F. Turini. Can We Trust Fair-AI?  Senior Member Presentation Track, The 37th AAAI Conference on Artificial Intelligence (AAAI 2023).
• I. Papageorgiou. Συστήματα Τεχνητής Νοημοσύνης και διακριτική μεταχείριση κατά το προσυμβατικό στάδιο σύναψης της σύμβασης εργασίας (eng. Artificial Intelligence (AI)-based systems and discrimination in recruitment). Efarmoges Astikou Dikaiou kai Politikis Dikonomias, Nomiki Bibliothiki Issue 10, October 2022. Greece.
• J. M. Alvarez, M. Russo. Perception as a Fairness Parameter. NeurIPS workshop on Algorithmic Fairness through the Lens of Causality and Privacy (AFCP2022).
• L. State, H. Salat, S. Rubrichi, Z. Smoreda. Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal. 2022 Trustworthy and Socially Responsible Machine Learning (TSRML 2022) co-located with Neural Information Processing Systems (NeurIPS) 2022
• C. Mougan, K. Broelemann, G. Kasneci, T. Tiropanis, S. Staab. Explanation Shift: Detecting distribution shifts on tabular data via the explanation space. NeurIPS’22 workshop on Distribution Shift.
• P. R. Lobo, E. Daga, H. Alani, M. Fernandez. Semantic Web technologies and bias in artificial intelligence: A systematic literature review. Semantic Web Journal, 2022.
• L. State. Constructing Meaningful Explanations: Logic-based Approaches. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22). https://dl.acm.org/doi/10.1145/3514094.3539544
• P. R. Lobo. Bias in Hate Speech and Toxicity Detection. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22). https://dl.acm.org/doi/pdf/10.1145/3514094.3539519
• K. M. Scott, S. M. Wang, M. Miceli, P. Delobelle, K. Sztandar-Sztanderska, B. Berendt. 2022. Algorithmic Tools in Public Employment Services: Towards a Jobseeker-Centric Perspective. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), June 21–24, 2022, Seoul, Republic of Korea. ACM, New York, NY, USA. https://doi.org/10.1145/3531146.3534631
• O. Lampridis, L. State,·R. Guidotti, S. Ruggieri. Explaining short text classification with diverse synthetic exemplars and counter‑exemplars. Springer Maching Learning (2022). https://doi.org/10.1007/s10994-022-06150-7
• A. Ferrara, L. Espín-Noboa, F. Karimi, C. Wagner. Link recommendations: Their impact on network structure and minorities. In 14th ACM Web Science Conference 2022 (WebSci ’22), June 26–29, 2022, Barcelona, Spain. https://doi.org/10.1145/3501247.3531583.
• C. Mougan, G. Kanellos, J. Micheler, J. Martinez, T. Gottron. Introducing explainable supervised machine learning into interactive feedback loops for statistical production system. IFC workshop on “Data science in central banking – data, technologies and applications” 2021.
• P. Naumann, E. Ntoutsi, Consequence-aware Sequential Counterfactual Generation, ECMLPKDD 2021.
• C. Yi, A. Zimek, E. Ntoutsi, XPROAX-Local explanations for text classification with progressive neighborhood approximation, DSAA 2021.
• C. Mougan, G. Kanellos, T. Gottron (2021). Desiderata for Explainable AI in statistical production systems of the European Central Ban. ECML-PKDD 2021 Workshop on BIAS.
• P. Delobelle, K. M. Scott, S. M. Wang, M. Miceli, D. Hartmann, T. Yang, E. Murasso, & B. Berendt (2021). Time to Question if We Should: Data-Driven and Algorithmic Tools in Public Employment Services. FEAST International Workshop on Fair, Effective And Sustainable Talent Management Using Data Science ECML-PKDD 2021 Workshop, 8.
• S. Fabbrizzi, S. Papadopoulos, E. Ntoutsi, I. Kompatsiaris (2021). A Survey on Bias in Visual Datasets.
• C. Mougan, D. Masip, J. Nin, O. Pujol (2021): Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems, arxiv
• K. Scott, P. Delobelle, B. Berendt (2021). Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT, arxiv
• O. Lampridis, R. Guidotti, S. Ruggieri. Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars. Discovery Science (DS 2020). 357-373. Vol. 12323 of LNCS, Springer, September 2020. Honorable mention at DS 2020. (Best Paper Award)
• T. Calders, E. Ntoutsi, M. Pechenizkiy, B. Rosenhahn, S. Ruggieri. Introduction to The Special Section on Bias and Fairness in AI. ACM SIGKDD Explorations Newsletter. Vol. 23 Issue 1, June 2021, 1-3.
• L. State: Logic Programming for XAI: A Technical Perspective. ICLP Workshops 2021. CEUR Workshop Proceedings vol. 2970.
• E. Ntoutsi, P. Fafalios, U. Gadiraju, V. Iosifidis, W. Nejdl, M. E. Vidal, … & I. Kompatsiaris (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
• E. Krasanakis, S. Papadopoulos, I. Kompatsiaris (2020). Applying Fairness Constraints on Graph Node Ranks under Personalization Bias, COMPLEX NETWORKS