AEQUITAS 2025
Fairness and Bias in AI

Table of Contents

• Summary: There were 27 papers submitted for peer-review to this workshop. Out of these, 14 papers were accepted for this volume, 14 as regular papers and 0 as short papers.

Papers

• Modeling Fairness in Recruitment AI via Information Flow
  Mattias Brännström , Themis Dimitra Xanthopoulou , Lili Jiang
• VB-Mitigator: An Open-source Framework for Evaluating and Advancing Visual Bias Mitigation
  Ioannis Sarridis , Christos Koutlis , Symeon Papadopoulos , Christos Diou
• Transparency and Proportionality in Post-Processing Algorithmic Bias Correction
  Juliett Suárez-Ferreira , Marija Slavkovik , Jorge Casillas
• Causal Synthetic Data Generation in Recruitment
  Andrea Iommi , Antonio Mastropietro , Riccardo Guidotti , Anna Monreale , Salvatore Ruggieri
• A Fairness-Oriented Visual Extension for the PSyKE Platform
  Federico Sabbatini
• Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
  Riccardo Cantini , Nicola Gabriele , Alessio Orsino , Domenico Talia
• First Ask Then Act (FATA): A Community-Centered Inclusive Approach for Hate Speech Detection
  Chiara Ferrando , Lia Draetta , Andrea Marra , Angela Zottola , Cristina Bosco , Viviana Patti
• How do doctors perceive AI in their medical practice?
  Laura Sartori , Chiara Binelli , Francesca Lizzi , Andrea Chincarini , Francesco Sensi , Alessandra Retico
• On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Models
  Alessandro Antonucci , Eric Rossetto , Ivan Duvnjak
• Towards a Partial Coverage of Neural Network Explanations Using Approximation Networks
  Mathieu Brassart , Laurent Simon
• Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
  Sofoklis Kitharidis , Cor J. Veenman , Thomas Bäck , Niki van Stein
• Evaluating and Mitigating Fairness Risks in Machine Learning under Structured Missing Data
  Tim Kochs , Andrea Castellani , Felix Lanfermann , Barbara Hammer
• Mechanistic Interpretability with SAEs: Probing Religion, Violence, and Geography in Large Language Models
  Katharina Simbeck , Mariam Mahran
• Bias Detection in Cultural Heritage Metadata: Preliminary Results from the IMAGES Project
  Angelo Oddi , Gianmauro Romagna , Riccardo Rasconi , Paola Panarese , Paolo De Gasperis