Conceptual models in Interactive Machine Learning

At the end of March I will be going to IUI 2015 to present my paper Applying the CASSM Framework to Improving End User Debugging of Interactive Machine Learning.

This talks about some work I’ve done applying Ann Blandford’s framework for analysing software Concept-based Analysis of Surface and Structural Misfits: CASSM to Interactive Machine Learning. It is a framework that looks at user concepts and how they relate to concepts present in the software. A really interesting element is that is separates concepts in the interface from concepts in the system, so concepts that are central in the underlying algorithm can be missing from the interface and concepts in the interface might not be well represented in the functioning of the system. This lead me to the idea that for interactive machine learning the learning algorithms used should be well aligned to the users concepts of the situation and they should also be well represented visually in the interface. This should make the system easier to use and in particular easier to debug when it goes wrong (because debugging requires a good conceptual model of the system). In order to do this I suggested a nearest neighbour learning algorithm would be well suited to a learning system for full body interaction because users thought in terms of whole poses, not individual features (which are common concepts in other learning algorithms) and it works with the original training data, which users understand well. It also lead us to develop the visualisation you can see in the image above.

If you are interested, here is the abstract and full reference.

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 both the learning algorithm and user interface were better aligned 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.

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

Gillies, Marco , Kleinsmith, Andrea and Brenton, Harry . 2015. ‘Applying the CASSM Framework to Improving End User Debugging of Interactive Machine Learning’. In: ACM Intelligent User Interfaces (IUI). Atlanta, United States.