AIMMES 2024
AI bias: Measurements, Mitigation, Explanation Strategies 2024

Table of Contents

• Summary: There were 24 papers submitted for peer-review to this workshop. Out of these, 6 papers were accepted for this volume, 6 as regular papers and 0 as short papers.

Papers

• A Leaderboard to Benchmark Ethical Biases in LLMs
  Marcos Gomez-Vazquez, Sergio Morales, German Castignani, Robert Clarisó, Aaron Conrardy, Louis Deladiennee, Samuel Renault, Jordi Cabot
• CO*IR: A Greedy and Individually Fair Re-ranker
  Seán Healy
• How a socio-technical approach to AI auditing can change how we understand and measure fairness in machine learning systems
  Gemma Galdon Clavell, Ariane Aumaitre, Toon Calders
• Analyzing bias and discrimination in an algorithmic hiring use case
  David Quer, Anna Via, Marc Serra-Vidal, Laia Nadal, Didac Fortuny
• Towards Standardizing AI Bias Exploration
  Emmanouil Krasanakis, Symeon Papadopoulos
• Optimal Fair Ranking: Real Performances and Critical Parameters
  Daniela A. Parletta, Fabio Napoli
• Credit scoring and transparency between the AI Act and the Court of Justice of the European Union
  Elena Falletti, Chiara Gallese
• Requirements of eXplainable AI in Algorithmic Hiring
  Andrea Beretta, Gianmario Ercoli, Alfonso Ferraro, Riccardo Guidotti, Andrea Iommi, Antonio Mastropietro, Anna Monreale, Daniela Rotelli, Salvatore Ruggieri