# How Do We Measure Trust in Visual Data Communication?

arXiv: https://arxiv.org/abs/2209.14276 · HTML: https://mlsanity.com/paper/2209.14276/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots](https://mlsanity.com/paper/2307.09330/) (2023)
- [Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots](https://mlsanity.com/paper/2407.11625/) (2024)

## Abstract (arXiv)

Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers' trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a "calibrated trust," at which readers arrive after critically evaluating the information presented. To systematically understand what drives readers to engage in "calibrated trust," we must first equip ourselves with reliable and valid methods for measuring trust. Computer science and data visualization researchers have not yet reached a consensus on a trust definition or metric, which are essential to building a comprehensive trust model in human-data interaction. On the other hand, social scientists and behavioral economists have developed and perfected metrics that can measure generalized and interpersonal trust, which the visualization community can reference, modify, and adapt for our needs. In this paper, we gather existing methods for evaluating trust from other disciplines and discuss how we might use them to measure, define, and model trust in data visualization research. Specifically, we discuss quantitative surveys from social sciences, trust games from behavioral economics, measuring trust through measuring belief updating, and measuring trust through perceptual methods. We assess the potential issues with these methods and consider how we can systematically apply them to visualization research.