To Classify is to Interpret: Building Taxonomies from Heterogeneous Data through Human-AI Collaboration

TL;DR

This paper presents a human-AI collaborative approach for taxonomy building that combines machine learning outputs with user expertise, demonstrated through real-world applications.

Contribution

It introduces an iterative system enabling users to incorporate multiple ML model outputs into their sensemaking process for taxonomy creation.

Findings

Effective integration of ML outputs with human judgment.

Enhanced user control in taxonomy development.

Successful application in real-world scenarios.

Abstract

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.