| Vol-3523 urn:nbn:de:0074-3523-0 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 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).

# AEQUITAS 2023
Fairness and Bias in AI

### Proceedings of the 1st Workshop on Fairness and Bias in AI
co-located with 26th European Conference on Artificial Intelligence (ECAI 2023)

### Kraków, Poland, October 1st, 2023.

Edited by

### Roberta Calegari *
Andrea Aler Tubella **
Gabriel González Castañe ***
Virginia Dignum **
Michela Milano *

* University of Bologna, Department of Computer Science and Engineering, Bologna, Italy
** Umeå University, Umeå, Sweden
*** University College of Cork, Cork, Ireland

## Table of Contents

- Preface
  Summary: There were 13 papers submitted for peer-review to this workshop. Out of these, 12 papers were accepted for this volume, 12 as regular papers and 0 as short papers.

### Papers

- Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
  Ludwig Bothmann, Susanne Dandl, Michael Schomaker
- Visualizing Bias in Activations of Deep Neural Networks as Topographic Maps
  Valerie Krug, Christopher Olson, Sebastian Stober
- Recommendations for Bias Mitigation Methods: Applicability and Legality
  Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu
- Reasoning With Bias
  Chiara Manganini, Giuseppe Primiero
- FAiRDAS: Fairness-Aware Ranking as Dynamic Abstract System
  Eleonora Misino, Roberta Calegari, Michele Lombardi, Michela Milano
- EXTRACT: Explainable Transparent Control of Bias in Embeddings
  Zhijin Guo, Zhaozhen Xu, Martha Lewis, Nello Cristianini
- Fairness in job recommendations: estimating, explaining, and reducing gender gaps
  Guillaume Bied, Christophe Gaillac, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Solal Nathan, Michèle Sebag,
- Gender Bias in Multimodal Models: A Transnational Feminist Approach Considering Geographical Region and Culture
  Abhishek Mandal, Suzanne Little, Susan Leavy
- A geometric framework for fairness
  Alessandro Maggio, Luca Giuliani, Roberta Calegari, Michele Lombardi, Michela Milano
- Symbolic AI (LFIT) for XAI to handle biases
  Javier Tello, Marina de la Cruz, Tony Ribeiro, Julian Fierrez, Aythami Morales, Ruben Tolosana, César Luis Alonso, Alfonso Ortega
- Bias Mitigation for Large Language Models using Adversarial Learning
  Jasmina S. Ernst, Sascha Marton, Jannik Brinkmann, Eduardo Vellasques, Damien Foucard, Martin Kraemer, Marian Lambert
- Impact based fairness framework for socio-technical decision making
  Mattias Brännström, Lili Jiang, Andrea Aler Tubella, Virginia Dignum

2023-10-06: submitted by Roberta Calegari, metadata incl. bibliographic data published under Creative Commons CC0
2023-10-25: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|