Titre / Title: CoDEX – Collaborative Design and Evaluation of eXplainable AI systems

Projet Ciblé (PC) / Targeted Project (PC): PC3 MATCHING – Collaboration with intelligent systems

Commentaires / Comments:

Etat du sujet / State of the subject: Disponible / Available

Date de publication / Publication date: 25/04/2025

Institution de rattachement / Institutional affiliation: CNRS – LAMIH (CNRS UMR 8201 – Univ. Polytechnique Hauts-de-France)

Résumé / Abstract: With the widespread use of machine learning for algorithmic decision-making, Explainable Artificial Intelligence (XAI) systems are increasingly important. However, they are not always understandable and trustworthy for end-users, who should know when to rely on AI’s advice to make informed decisions. We advocate that to better address this problem the design of the XAI system should be done in a collaborative way among the different stakeholders (AI experts, Human-computer iteraction designers, domain experts and end-users. Moreover, the decision making should be done in collaboration during the interaction of end-user and the XAI system.

Détails du sujet / Subject details: PDF

Directeur / directrice de thèse / Main advisor: Kathia Marçal de Oliveira (kathia.oliveira@uphf.fr) – LAMIH

Encadrant(e) de thèse / Secondary advisor: Rafik Belloum (rafik.belloum@uphf.fr) – LAMIH

Autre Encadrant(e) de thèse / Additional advisor:

Pour faire acte de candidature sur ce sujet, veuillez écrire aux auteurs directement / To apply on this subject, please write directly to the authors.