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ORCIDAEQUITAS@ECAI 2024: Santiago de Compostela, Spain
- Roberta Calegari, Virginia Dignum, Barry O'Sullivan:
Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024), Santiago de Compostela, Spain, October 20th, 2024. CEUR Workshop Proceedings 3808, CEUR-WS.org 2024 - Bastiaan Bruinsma, Annika Fredén, Kajsa Hansson, Moa Johansson, Pasko Kisic-Merino, Denitsa Saynova:
Setting the AI Agenda - Evidence from Sweden in the ChatGPT Era. - Jingyu Hu, Jun Hong, Mengnan Du, Weiru Liu:
ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups. - Lea Cohausz, Jakob Kappenberger, Heiner Stuckenschmidt:
Combining Fairness and Causal Graphs to Advance Both. - Moisés Santos, André C. P. L. F. de Carvalho, Carlos Soares:
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation. - Mayra Russo, Maria-Esther Vidal:
Leveraging Ontologies to Document Bias in Data. - Manh Khoi Duong, Stefan Conrad:
Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes. - Guillermo Villate-Castillo, Borja Sanz, Javier Del Ser:
Mitigating Toxicity in Dialogue Agents through Adversarial Reinforcement Learning. - Matteo Magnini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini:
Enforcing Fairness via Constraint Injection with FaUCI. - Gabriele La Malfa, Jie M. Zhang, Michael Luck, Elizabeth Black:
Using Protected Attributes to Consider Fairness in Multi-Agent Systems. - Aashutosh Ganesh, Mirela Popa, Daan Odijk, Nava Tintarev:
Does spatio-temporal information benefit the video summarization task? - Luca Giuliani, Eleonora Misino, Roberta Calegari, Michele Lombardi:
Long-Term Fairness Strategies in Ranking with Continuous Sensitive Attributes. - Camilla Quaresmini, Giuseppe Primiero:
Data Quality Dimensions for Fair AI. - Federico Sabbatini, Roberta Calegari:
Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models. - Thierry Poibeau:
Bias, Subjectivity and Norm in Large Language Models. - Marion Bartl, Susan Leavy:
From Inclusive Language to Inclusive AI: A Proof-of-Concept Study into Pre-Trained Models. - Md. Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz:
FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability. - Angel S. Marrero, Gustavo A. Marrero, Carlos Bethencourt, Liam James, Roberta Calegari:
AI-fairness and equality of opportunity: a case study on educational achievement.
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