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<!DOCTYPE html>
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<title>AEQUITAS 2023 | Workshop on Fairness and Bias in AI</title>
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Proceedings of AEQUITAS 2023 are available on CEUR.ws
<a style="text-align: justify; color :black" href="http://ceur-ws.org/Vol-3523/"><h3>Go To The Proceedings</h3></a>
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<h2 class="noo-sh-title-top">Accepted Papers</span></h2>
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<ul>
<li><i>Causal Fair Machine Learning via Rank-Preserving Interventional Distributions</i> -
Ludwig Bothmann, Susanne Dandl, Michael Schomaker</li><br>
<li><i>Visualizing Bias in Activations of Deep Neural Networks as Topographic Maps</i> -
Valerie Krug, Christopher Olson, Sebastian Stober</li><br>
<li><i>Recommendations for Bias Mitigation Methods: Applicability and Legality</i> -
Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu</li><br>
<li><i>Reasoning With Bias</i> -
Chiara Manganini, Giuseppe Primiero</li><br>
<li><i>FAiRDAS: Fairness-Aware Ranking as Dynamic Abstract System</i> -
Eleonora Misino, Roberta Calegari, Michele Lombardi, Michela Milano</li><br>
<li><i>EXTRACT: Explainable Transparent Control of Bias in Embeddings</i> -
Zhijin Guo, Zhaozhen Xu, Martha Lewis, Nello Cristianini</li><br>
<li><i>Fairness in job recommendations: estimating, explaining, and reducing gender gaps</i> -
Guillaume Bied, Christophe Gaillac, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Solal Nathan, Michèle Sebag</li><br>
<li><i>Gender Bias in Multimodal Models: A Transnational Feminist Approach Considering Geographical Region and Culture</i> -
Abhishek Mandal, Suzanne Little, Susan Leavy</li><br>
<li><i>A geometric framework for fairness</i> -
Alessandro Maggio, Luca Giuliani, Roberta Calegari, Michele Lombardi, Michela Milano</li><br>
<li><i>Symbolic AI (LFIT) for XAI to handle biases</i> -
Javier Tello, Marina de la Cruz, Tony Ribeiro, Julian Fierrez, Aythami Morales, Ruben Tolosana, César Luis Alonso, Alfonso Ortega</li><br>
<li><i>Bias Mitigation for Large Language Models using Adversarial Learning</i> -
Jasmina S. Ernst, Sascha Marton, Jannik Brinkmann, Eduardo Vellasques, Damien Foucard, Martin Kraemer, Marian Lambert</li><br>
<li><i>Impact based fairness framework for socio-technical decision making</i> -
Mattias Brännström, Lili Jiang, Andrea Aler Tubella, Virginia Dignum</li><br>
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<li>26th European Conference on Artificial Intelligence
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