The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications

Stef van der Elzen, Gennady L. Andrienko, Natalia Andrienko, Brian Fisher, Rafael Martins, Jaakko Peltonen, Alex Telea, Michel Verleysen

Research output: Contribution to journalArticleAcademicpeer-review


We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user’s mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users’ expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline.
Original languageEnglish
Pages (from-to)78-88
Number of pages11
JournalIEEE Computer Graphics and Applications
Issue number2
Publication statusPublished - 1 Mar 2023


Dive into the research topics of 'The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications'. Together they form a unique fingerprint.

Cite this