Original: https://ceur-ws.org/Vol-3744/ | Mirror: https://ceur-ws.wikidata.dbis.rwth-aachen.de/Vol-3744/ | Year: 2024

Vol-3744
urn:nbn:de:0074-3744-7

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 https://creativecommons.org/licenses/by/4.0/.

AIMMES 2024

Proceedings of the 1st Workshop on AI bias: Measurements, Mitigation, Explanation Strategies
co-located with AI Fairness Cluster Inaugural Conference 2024

Amsterdam, Netherlands, March 20, 2024.

Edited by

Roberta Calegari
Carlos Castillo
Symeon Papadopoulos
Roger Soraa

Table of Contents

PDF
A Leaderboard to Benchmark Ethical Biases in LLMs PDF
Louis Deladiennee, German Castignani, Samuel Renault, Marcos Gomez-Vazquez, Aaron David Conrardy, Jordi Cabot, Sergio Morales 0001, Robert Clarisó
CO*IR: A Greedy and Individually Fair Re-ranker PDF
Sean Healy
How a socio-technical approach to AI auditing can change how we understand and measure fairness in machine learning systems PDF
Gemma Galdon Clavell, Ariane Aumaitre, Toon Calders
Analyzing bias and discrimination in an algorithmic hiring use case PDF
Laia Nadal, Marc Serra-Vidal, Didac Fortuny, Anna Via, David Quer
Towards Standardizing AI Bias Exploration PDF
Emmanouil Krasanakis, Symeon Papadopoulos
Optimal Fair Ranking: Real Performances and Critical Parameters PDF
Daniela A. Parletta, Fabio Napoli
Credit scoring and transparency between the AI Act and the Court of Justice of the European Union PDF
Elena Falletti, Chiara Gallese
Requirements of eXplainable AI in Algorithmic Hiring PDF
Salvatore Ruggieri, Anna Monreale, Gianmario Ercoli, Andrea Iommi, Antonio Mastropietro, Daniela Rotelli, Andrea Beretta, Riccardo Guidotti, Alfonso Ferraro

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