Applying the CASSM Framework to Improving End User Debugging of Interactive Machine Learning





Citation of Original Publication

Gillies, Marco, Andrea Kleinsmith, and Harry Brenton. “Applying the CASSM Framework to Improving End User Debugging of Interactive Machine Learning.” In Proceedings of the 20th International Conference on Intelligent User Interfaces, 181–85. IUI ’15. New York, NY, USA: Association for Computing Machinery, 2015.


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This paper presents an application of the CASSM (Concept-based Analysis of Surface and Structural Misfits) framework to interactive machine learning for a bodily interaction domain. We developed software to enable end users to design full body interaction games involving interaction with a virtual character. The software used a machine learning algorithm to classify postures as based on examples provided by users. A longitudinal study showed that training the algorithm was straightforward, but that debugging errors was very challenging. A CASSM analysis showed that there were fundamental mismatches between the users concepts and the working of the learning system. This resulted in a new design in which aimed to better align both the learning algorithm and user interface with users' concepts. This work provides and example of how HCI methods can be applied to machine learning in order to improve its usability and provide new insights into its use.