To Classify is to Interpret: Building Taxonomies from Heterogeneous Data through Human-AI Collaboration
This addresses the need for interpretable and user-inclusive systems in knowledge organization, though it is incremental in combining existing HCI and ML concepts.
The paper tackles the problem of automating taxonomy building from heterogeneous data by proposing a human-AI collaborative approach that integrates machine learning outputs into user sensemaking, implemented in two real-world use cases.
Taxonomy building is a task that requires interpreting and classifying data within a given frame of reference, which comes to play in many areas of application that deal with knowledge and information organization. In this paper, we explore how taxonomy building can be supported with systems that integrate machine learning (ML). However, relying only on black-boxed ML-based systems to automate taxonomy building would sideline the users' expertise. We propose an approach that allows the user to iteratively take into account multiple model's outputs as part of their sensemaking process. We implemented our approach in two real-world use cases. The work is positioned in the context of HCI research that investigates the design of ML-based systems with an emphasis on enabling human-AI collaboration.