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Conference Papers Year : 2021

How do People Train a Machine? Strategies and (Mis)Understandings


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.
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Dates and versions

hal-03182950 , version 1 (26-03-2021)



Téo Sanchez, Baptiste Caramiaux, Jules Françoise, Frédéric Bevilacqua, Wendy Mackay. How do People Train a Machine? Strategies and (Mis)Understandings. CSCW 2021 - The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, Oct 2021, Virtual, United States. ⟨10.1145/3449236⟩. ⟨hal-03182950⟩
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