Challenges in Evaluating Interactive Visual Machine Learning Systems

N. Boukhelifa, A. Bezerianos, R. Chang, C. Collins, S. Drucker, A. Endert, J. Hullman, C. North, M. Sedlmair

Research output: Contribution to journalArticlepeer-review

Abstract

In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.

Original languageEnglish (US)
Article number9238590
Pages (from-to)88-96
Number of pages9
JournalIEEE Computer Graphics and Applications
Volume40
Issue number6
DOIs
StatePublished - Nov 1 2020

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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