%0 Journal Article
%@ 2573-0142
%A Milana, Federico
%A Costanza, Enrico
%A Musolesi, Mirco
%A Ayobi, Amid
%D 2025
%F discovery:10206159
%J Proceedings of the ACM on Human-Computer Interaction
%K Human-centered computing, Interactive systems and tools; Computing methodologies, Machine learning, interactive machine learning, thematic analysis
%N 2
%T Understanding Interaction with Machine Learning through  a Thematic Analysis Coding Assistant: A User Study
%U https://discovery.ucl.ac.uk/id/eprint/10206159/
%V 9
%X Interactive Machine Learning (ML) enables users, including non-experts in ML, to iteratively train and improve  ML models. However, limited research has been reported on how non-experts interact with these systems.  Focusing on thematic analysis as a practical application, we report on a user study where 20 participants  interacted with TACA, a functioning Interactive ML tool. Thematic analysis involves individual interpretation  of ambiguous data, hence it is suited for and can benefit from the iterative customization of models supported  by Interactive ML. Through a combination of interaction logs and semi-structured interviews, our findings  revealed that, by using TACA, participants critically reflected on their analysis, gained new thematic insights,  and adapted their interpretative stance. We also document misconceptions of ML concepts, positivist views,  and personal blame for poor model performance. We then discuss how applications could be designed to  improve the understanding of Interactive ML concepts and foster reflexive work practices.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.