eprintid: 10206159 rev_number: 12 eprint_status: archive userid: 699 dir: disk0/10/20/61/59 datestamp: 2025-03-17 10:20:04 lastmod: 2025-03-17 12:15:24 status_changed: 2025-03-17 10:20:04 type: article metadata_visibility: show sword_depositor: 699 creators_name: Milana, Federico creators_name: Costanza, Enrico creators_name: Musolesi, Mirco creators_name: Ayobi, Amid title: Understanding Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study ispublished: inpress divisions: UCL divisions: B02 divisions: C07 divisions: D05 divisions: F70 keywords: Human-centered computing, Interactive systems and tools; Computing methodologies, Machine learning, interactive machine learning, thematic analysis note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: 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. date: 2025-04 date_type: published official_url: https://dl.acm.org/journal/pacmhci oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2370684 lyricists_name: Costanza, Enrico lyricists_id: ECOST53 actors_name: Costanza, Enrico actors_id: ECOST53 actors_role: owner funding_acknowledgements: EP/N509577/1 [Engineering and Physical Sciences Research Council] full_text_status: public publication: Proceedings of the ACM on Human-Computer Interaction volume: 9 number: 2 article_number: CSCW197 issn: 2573-0142 citation: Milana, Federico; Costanza, Enrico; Musolesi, Mirco; Ayobi, Amid; (2025) Understanding Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study. Proceedings of the ACM on Human-Computer Interaction , 9 (2) , Article CSCW197. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10206159/1/Understanding_Interaction_with_Machine_Learning_through_a_Thematic_Analysis_Coding_Assistant__FINAL_.pdf