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 the Creative Commons License Attribution 4.0 International (CC BY 4.0).

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

Proceedings of the 1st Workshop on AI bias: Measurements, Mitigation, Explanation Strategies

co-located with the AI Fairness Cluster Inaugural Conference 2024

Amsterdam, Netherlands, March 20, 2024.

Edited by

Roberta Calegari *
Carlos Castillo
Symeon Papadopoulos *
Roger Soraa ****

* University of Bologna, Department of Computer Science and Engineering, Bologna, Italy
** ICREA and Universitat Pompeu Fabra -, Barcelona, Spain
*** CERTH/ITI , Thessaloniki, Greece
*** NTNU Centre for Technology and Society , Trondheim, Norway

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

2024-08-06: submitted by Roberta Calegari, metadata incl. bibliographic data published under Creative Commons CC0
2024-08-21: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|