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Building and Eroding: Exogenous and Endogenous Factors that Influence Subjective Trust in Visualization

R. Jordan Crouser, Syrine Matoussi, Lan Kung, Saugat Pandey, Oen G. McKinley, Alvitta Ottley

TL;DR

The paper investigates how subjective trust in data visualizations arises from both endogenous design features and exogenous viewer characteristics. It reanalyzes Pandey et al.'s data using deviation-from-average as the trust metric and applies recursive partitioning and random forests to identify predictors. The study finds visualization type (endogenous) and visualization literacy (exogenous) as primary predictors, with nontrivial interactions between them, and shows combined effects across trust dimensions. These insights yield design recommendations for personalized and adaptive visualizations and highlight the importance of improving data literacy to enhance trustworthy visualization practices.

Abstract

Trust is a subjective yet fundamental component of human-computer interaction, and is a determining factor in shaping the efficacy of data visualizations. Prior research has identified five dimensions of trust assessment in visualizations (credibility, clarity, reliability, familiarity, and confidence), and observed that these dimensions tend to vary predictably along with certain features of the visualization being evaluated. This raises a further question: how do the design features driving viewers trust assessment vary with the characteristics of the viewers themselves? By reanalyzing data from these studies through the lens of individual differences, we build a more detailed map of the relationships between design features, individual characteristics, and trust behaviors. In particular, we model the distinct contributions of endogenous design features (such as visualization type, or the use of color) and exogenous user characteristics (such as visualization literacy), as well as the interactions between them. We then use these findings to make recommendations for individualized and adaptive visualization design.

Building and Eroding: Exogenous and Endogenous Factors that Influence Subjective Trust in Visualization

TL;DR

The paper investigates how subjective trust in data visualizations arises from both endogenous design features and exogenous viewer characteristics. It reanalyzes Pandey et al.'s data using deviation-from-average as the trust metric and applies recursive partitioning and random forests to identify predictors. The study finds visualization type (endogenous) and visualization literacy (exogenous) as primary predictors, with nontrivial interactions between them, and shows combined effects across trust dimensions. These insights yield design recommendations for personalized and adaptive visualizations and highlight the importance of improving data literacy to enhance trustworthy visualization practices.

Abstract

Trust is a subjective yet fundamental component of human-computer interaction, and is a determining factor in shaping the efficacy of data visualizations. Prior research has identified five dimensions of trust assessment in visualizations (credibility, clarity, reliability, familiarity, and confidence), and observed that these dimensions tend to vary predictably along with certain features of the visualization being evaluated. This raises a further question: how do the design features driving viewers trust assessment vary with the characteristics of the viewers themselves? By reanalyzing data from these studies through the lens of individual differences, we build a more detailed map of the relationships between design features, individual characteristics, and trust behaviors. In particular, we model the distinct contributions of endogenous design features (such as visualization type, or the use of color) and exogenous user characteristics (such as visualization literacy), as well as the interactions between them. We then use these findings to make recommendations for individualized and adaptive visualization design.
Paper Structure (20 sections, 4 figures)

This paper contains 20 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Contributions
  3. Background
  4. Endogenous Factors
  5. Visualization Type
  6. Visual Embellishments
  7. Color
  8. Exogenous Features
  9. Individual Differences
  10. Visualization Literacy
  11. Education
  12. Methodology
  13. Data Cleaning
  14. Calculating Deviation-from-Average
  15. Recursive Partitioning and Random Forests
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Visualization Types
  • Figure 2: We observed a weak correlation between participants' visualization literacy and education. Samples in each education level were balanced for age and sex.
  • Figure 3: A representative tree depicting the influence of endogenous features on subjective trust ratings (higher or lower than average).
  • Figure 4: A representative tree depicting the influence of exogenous features on subjective trust ratings (higher or lower than average).