Replay vidéo de la journée 🎥
Les présentations et débats de la journée sont maintenant disponibles en replay sur la chaîne YouTube de la journée IHM-IA !
Intervenant·e
Eya Ben Chaaben (organisatrice)
Doctorante à l’Université Paris-Saclay, LISN, équipe ExSitu, CNRS, Inria
Eya est doctorante à l’Université Paris-Saclay, dans l’équipe ExSitu du LISN. Sa thèse porte sur l’IHM pour la soutenabilité en apprentissage machine et co-organise la journée IHM-IA, principalement la session de posters et démonstrations.
Ph.D. Description: During the life-cycle of developing ML models, there are avoidable processes if sufficient knowledge such as best practices, efficient algorithms or hardware alternatives are available for the developers. We define these processes as ML-waste, borrowing from the e-waste concept. Unlike e-waste which is mostly tangible and can be recycled, ML-waste is intangible, and causes irreversible real-world energy and resource consumption and leaves CO2 footprints. The goal of my thesis is to create new interaction and visualization approaches that will allow users to interactively explore the trade-offs of competing ML models together with intelligent agents. Exploring ML model alternatives during the development process, before the models enter the training cycles, requires users to express potentially ambiguous project objectives and to understand the trade-offs of ML model alternatives, e.g. time, computing hardware, or estimated CO2 footprint for a particular task.