Publications

• M. Fahimi, M. Russo, K. M. Scott, M. Vidal, B. Berendt, K. Kinder-Kurlanda. Articulation Work and Tinkering for Fairness in Machine Learning. The 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing. 2024.
• J. M. Alvarez, A. B. Colmenarejo, A. Elobaid, S. Fabbrizzi, M. Fahimi, A. Ferrara, S. Ghodsi, C. Mougan, I. Papageorgiou, P. Reyero, M. Russo, K. M. Scott, L. State, X. Zhao & S. Ruggieri. Policy advice and best practices on bias and fairness in AI. Ethics and Information Technology. Springer 2024.
• A. Ferrara, F. Bonchi, F, Fabbri, F. Karimi, C. Wagner. Bias-aware ranking from pairwise comparisons. Data Mining and Knowledge Discovery Journal, Springer. 2024.
• A. Elobaid, N. Ramoly, L. Younes, S. Papadopoulos, E. Ntoutsi, I. Kompatsiaris. Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure. The 1st International Workshop on Responsible Face Image Processing at IEEE FG24.
• S. Ghodsi, S. A. Seyedi, E. Ntoutsi. Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering. PAKDD 2024.
• J. M. Alvarez, A. B. Colmenarejo, A. Elobaid, S. Fabbrizzi, M. Fahimi, A. Ferrara, S. Ghodsi, C. Mougan, I. Papageorgiou, P. Reyero, M. Russo, K. M. Scott, L. State, X. Zhao, S. Ruggieri. Policy advice and best practices on bias and fairness in AI. Ethics and Information Technology. Springer Journals. 2024.
• X. Zhao, K. Broelemann, S. Ruggieri, G. Kasneci. Causal Fairness-Guided Dataset Reweighting using Neural Networks. 2023 IEEE International Conference on Big Data in Sorrento, Italy.
• C. Mougan, R. Plant, C. Teng, M. Bazzi, A. Cabrejas-Egea, R. S. Chan, D. S. Jasin, M. Stoffel, K. J. Whitaker, J. Manser. How to Data in Datathons. NeurIPS 2023 Track on Datasets and Benchmark.
• S. Fabbrizzi, X. Zhao, E. Krasanakis, S. Papadopoulos, E. Ntoutsi. Studying bias in visual features through the lens of optimal transport. ECML PKDD 2023.
• L. State, S. Ruggieri and F. Turini. Declarative Reasoning on Explanations Using Constraint Logic Programming. European Conference on Logics in Artificial Intelligence (JELIA ’23).
• A. G. Yasar, A. Chong, E. Dong, T. K. Gilbert, S. Hladikova, R. Maio, C. Mougan, X. Shen, S. Singh, A. Stoica, S. Thais, M. Zilka. AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI. GenLaw ’23.
• L. State, S. Ruggieri, F. Turini. Reason to explain: Interactive contrastive explanations (REASONX). In the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023).
• J. M. Alvarez, S. Ruggieri. Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference. The ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) 2023.
• S. Ghodsi, E. Ntoutsi. Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy. European Workshop on Algorithmic Fairness (EWAF’23).
• L. State, A. B. Colmenarejo, A. Beretta, S. Ruggieri, F. Turini, S. Law. The Explanation Dialogues: Understanding how Legal Experts Reason About XAI Methods. European Workshop on Algorithmic Fairness (EWAF’23).
• L. State, M. Fahimi. Careful Explanations: A Feminist Perspective on XAI. European Workshop on Algorithmic Fairness (EWAF’23).
• J. M. Alvarez, K. M. Scott, S. Ruggieri, B. Berendt. Domain Adaptive Decision Trees: Implications for Accuracy and Fairness. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23).
• C. Mougan, J. M. Alvarez, S. Ruggieri, S. Staab. Fairness Implications of Encoding Protected Categorical Attributes. The 6th AAAI/ACM Conference on AI, Ethics, and Society (AIES’23).
• F. Castillo-Eslava, C. Mougan, A. Romero-Eche, S. Staab. The Role of Large Language Models in the Recognition of Territorial Sovereignty: An Analysis of the Construction of Legitimacy. The European Workshop on Algorithmic Fairness (EWAF’22).
• C. Mougan, L. State, A. Ferrara, S. Ruggieri, S. Staab. Demographic Parity Inspector: Fairness Audits via the Explanation Space. The European Workshop on Algorithmic Fairness (EWAF’22).
• 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