City Research Online

Visual Analytics for Human-Centered Machine Learning

Andrienko, N. ORCID: 0000-0003-3313-1560, Andrienko, G. ORCID: 0000-0002-8574-6295, Adilova, L. , Wrobel, S. & Rhyne, T-M. (2022). Visual Analytics for Human-Centered Machine Learning. IEEE Computer Graphics and Applications, 42(1), pp. 123-133. doi: 10.1109/MCG.2021.3130314


We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.

Publication Type: Article
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Computer science, Bridges, Computational modeling, Visual analytics, Human intelligence, Buildings, Machine learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Departments: School of Science & Technology > Computer Science
Text - Accepted Version
Download (606kB) | Preview



Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login