1. How do People Train a Machine? Strategies and (Mis)Understandings
- Author
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Frédéric Bevilacqua, Téo Sanchez, Jules Françoise, Baptiste Caramiaux, Wendy E. Mackay, Extreme Situated Interaction (EX-SITU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Architectures et Modèles pour l'Interaction (AMI), Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This research was supported by the ELEMENT project (ANR-18- CE33-0002) from the French National Research Agency and the CNRS-funded project INTACT under the PEPS programme. We want to acknowledge and thank everyone who was involved in each stage of the research, in particular the anonymous reviewers and the participants of the study. We especially want to express our sincere gratitude to Matteo Merzagora, Aude Ghilbert, Paul Boniface and Arnaud Malher from the association TRACES and the Projet Siscode (Horizon 2020 Research and Innovation programme, grant agreement N° 788217), whose collaboration made this study possible. Thanks to Gianni Franchi for his useful thoughts on uncertainty in Deep Neural Networks., ANR-18-CE33-0002,ELEMENT,Stimuler l'Apprentissage de Mouvements dans les Interactions Humain-Machine(2018), European Project: 788217,SISCODE(2018), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH)
- Subjects
Computer Networks and Communications ,Computer science ,Sketch recognition ,Interactive Machine Learning ,02 engineering and technology ,Task (project management) ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Human–computer interaction ,Agency (sociology) ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] ,Protocol (object-oriented programming) ,050107 human factors ,Human-centered analysis ,SIMPLE (military communications protocol) ,Artificial neural network ,05 social sciences ,020207 software engineering ,Interactive technology ,Human-AI Interaction ,Sketch ,Human-Computer Interaction ,ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI) ,Social Sciences (miscellaneous) - Abstract
International audience; Machine learning systems became pervasive in modern interactive technology but provide users with little, if any, agency with respect to how their models are trained from data. In this paper, we are interested in the way novices handle learning algorithms, what they understand from their behavior and what strategy they may use to "make it work". We developed a web-based sketch recognition algorithm based on Deep Neural Network (DNN), called Marcelle-Sketch, that end-users can train incrementally. We present an experimental study that investigate people's strategies and (mis)understandings in a realistic algorithm-teaching task. Our study involved 12 participants who performed individual teaching sessions using a think-aloud protocol. Our results show that participants adopted heterogeneous strategies in which variability affected the model performances. We highlighted the importance of sketch sequencing, particularly at the early stage of the teaching task. We also found that users' understanding is facilitated by simple operations on drawings, while confusions are caused by certain inherent properties of DNN. From these findings, we propose implications for design of IML systems dedicated to novices and discuss the socio-cultural aspect of this research.
- Published
- 2021
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