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