|  | Vol-3808 urn:nbn:de:0074-3808-1 Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# AEQUITAS 2024
Fairness and Bias in AI

### 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.

Edited by

### Roberta Calegari *
Virginia Dignum **
Barry O'Sullivan ***

* University of Bologna, Department of Computer Science and Engineering, Bologna, Italy
** Umeå University, Umeå, Sweden
*** University College of Cork, Cork, Ireland

## Table of Contents

- Summary: There were 35 papers submitted for peer-review to this workshop. Out of these, 17 papers were accepted for this volume, 17 as regular papers and 0 as short papers.

### Papers

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

2024-10-19: submitted by Roberta Calegari, metadata incl. bibliographic data published under Creative Commons CC0
2024-10-29: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|