Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Doh, Miriam"'
Autor:
Albert, Julien, Balfroid, Martin, Doh, Miriam, Bogaert, Jeremie, La Fisca, Luca, De Vos, Liesbet, Renard, Bryan, Stragier, Vincent, Jean, Emmanuel
Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering
Externí odkaz:
http://arxiv.org/abs/2409.06297
Autor:
Doh, Miriam, Karagianni, and Anastasia
This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations. Drawing on the "averageness theory," which suggests a relationship between a face's attractiveness and
Externí odkaz:
http://arxiv.org/abs/2407.17474
Autor:
Doh, Miriam, Rodrigues, Caroline Mazini, Boutry, Nicolas, Najman, Laurent, Mancas, Matei, Bersini, Hugues
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial In
Externí odkaz:
http://arxiv.org/abs/2403.08789
Autor:
Stassin, Sédrick, Englebert, Alexandre, Nanfack, Géraldin, Albert, Julien, Versbraegen, Nassim, Peiffer, Gilles, Doh, Miriam, Riche, Nicolas, Frenay, Benoît, De Vleeschouwer, Christophe
EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerged in recent years. Consequently, a subfield related to the evaluation
Externí odkaz:
http://arxiv.org/abs/2305.16361