DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples

Qianwen Wang, Sehi L'Yi, Nils Gehlenborg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.

Original languageEnglish (US)
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394215
DOIs
StatePublished - Apr 19 2023
Externally publishedYes
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: Apr 23 2023Apr 28 2023

Publication series

NameProceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Conference

Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Country/TerritoryGermany
CityHamburg
Period4/23/234/28/23

Bibliographical note

Publisher Copyright:
© 2023 Owner/Author.

Keywords

  • Human-AI collaboration
  • Visual exploration
  • XAI
  • latent space
  • small multiples

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