Cindy Xiong Bearfield

Trust plays a critical role in visual data communication and decision-making, yet existing visualization research employs varied trust measures, making it challenging to compare and synthesize findings across studies. In this work, we first took a bottom-up, data-driven approach to understand what visualization readers mean when they say they “trust” a visualization. We compiled and adapted a broad set of trust-related statements from existing inventories and collected responses to visualizations with varying degrees of trustworthiness. Through exploratory factor analysis, we derived an operational definition of trust in visualizations. Our findings indicate that people perceive a trustworthy visualization as one that presents credible information and is comprehensible and usable. Building on this insight, we developed an eight-item inventory: four core items measuring trust in visualizations and four optional items controlling for individual differences in baseline trust tendency. We established the inventory’s internal consistency reliability using McDonald’s omega, confirmed its content validity by demonstrating alignment with theoretically-grounded trust dimensions, and validated its criterion validity through two trust games with real-world stakes. Finally, we illustrate how this standardized inventory can be applied across diverse visualization research contexts. Utilizing our inventory, future research can examine how design choices, tasks, and domains influence trust, and how to foster appropriate trusting behavior in human-data interactions.

Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five state-of the-art visualization tools (LIME, SHAP, CP, Anchors, and ELI5) for model explainability, measuring how taxonomy characteristics affect comprehension, bias perception, and trust for non-expert ML users. Surprisingly, we find an inverse relationship between comprehension and trust: the better users understand the models, the less they trust them. We investigate the cause and find that this relationship is strongly mediated by bias perception: more comprehensible visualizations increase people's perception of bias, and increased bias perception reduces trust. We confirm this relationship is causal: Manipulating explainability visualizations to control comprehension, bias perception, and trust, we show that visualization design can significantly (p < 0.001) increase comprehension, increase perceived bias, and reduce trust. Conversely, reducing perceived model bias, either by improving model fairness or by adjusting visualization design, significantly increases trust even when comprehension remains high. Our work advances understanding of how comprehension affects trust and systematically investigates visualization's role in facilitating responsible ML applications.

Accounting for individual differences can improve the effectiveness of visualization design. While the role of visual attention in visualization interpretation is well recognized, existing work often overlooks how this behavior varies based on visual literacy levels. Based on data from a 235-participant user study covering three visualization tests (mini-VLAT, CALVI, and SGL), we show that distinct attention patterns in visual data exploration can correlate with participants' literacy levels: While experts (high-scorers) generally show a strong attentional focus, novices (low-scorers) focus less and explore more. We then propose two computational models leveraging these insights: Lit2Sal - a novel visual saliency model that predicts observer attention given their visualization literacy level, and Sal2Lit - a model to predict visual literacy from human visual attention data. Our quantitative and qualitative evaluation demonstrates that Lit2Sal outperforms state-of-the-art saliency models with literacy-aware considerations. Sal2Lit predicts literacy with 86% accuracy using a single attention map, providing a time-efficient supplement to literacy assessment that only takes less than a minute. Taken together, our unique approach to consider individual differences in salience models and visual attention in literacy assessments paves the way for new directions in personalized visual data communication to enhance understanding.

A growing body of work on visualization affordances highlights how specific design choices shape reader takeaways from information visualizations. However, mapping the relationship between design choices and reader conclusions often requires labor-intensive crowdsourced studies, generating large corpora of free-response text for analysis. To address this challenge, we explored alternative scalable research methodologies to assess chart affordances. We test four elicitation methods from human-subject studies: free response, visualization ranking, conclusion ranking, and salience rating, and compare their effectiveness in eliciting reader interpretations of line charts, dot plots, and heatmaps. Overall, we find that while no method fully replicates affordances observed in free-response conclusions, combinations of ranking and rating methods can serve as an effective proxy at a broad scale. The two ranking methodologies were influenced by participant bias towards certain chart types and the comparison of suggested conclusions. Rating conclusion salience could not capture the specific variations between chart types observed in the other methods. To supplement this work, we present a case study with GPT-4o, exploring the use of large language models (LLMs) to elicit human-like chart interpretations. This aligns with recent academic interest in leveraging LLMs as proxies for human participants to improve data collection and analysis efficiency. GPT-4o performed best as a human proxy for the salience rating methodology but suffered from severe constraints in other areas. Overall, the discrepancies in affordances we found between various elicitation methodologies, including GPT-4o, highlight the importance of intentionally selecting and combining methods and evaluating trade-offs.

Effective data visualizations enhance perception, support cognitive processing, and facilitate informed decision-making by Color increased credibility. aligning with human perceptual strengths. Conversely, poorly designed visualizations can impede comprehension, introduce interpretive bias, and diminish the perceived credibility of the conveyed message. This paper investigates the extent to which visual embellishments Hand-drawn font lowered credibility. influence perceived message credibility in data visualizations. We conducted two crowdsourced experiments to examine both holistic and component-level effects of embellishment. In the first experiment, participants evaluated the relative credibility of plain bar charts versus two embellished variants—cartoon-style and image-style—across topics. Participants provided both comparative judgments and qualitative feedback. In the second experiment, we systematically isolated the influence of specific design elements—color, font, and bar style—on credibility perceptions through controlled variations. Our findings reveal that the impact of embellishments on perceived message credibility is complex and context-dependent. While certain embellishments, such as the use of color and image style bars, enhanced credibility, others—most notably hand-drawn fonts and cartoon-style bars—significantly undermined it. By operationalizing trust through the lens of message credibility, this work offers empirical insight into the design factors that shape viewers' perceptions. We conclude by proposing actionable design guidelines to support the creation of visualizations that are effective for communication and credible.

Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five state-of-the-art visualization tools (LIME, SHAP, CP, Anchors, and ELI5) for model explainability, measuring how taxonomy characteristics affect comprehension, bias perception, and trust for non-expert ML users. Surprisingly, we find an inverse relationship between comprehension and trust: the better users understand the models, the less they trust them. We investigate the cause and find that this relationship is strongly mediated by bias perception: more comprehensible visualizations increase people's perception of bias, and increased bias perception reduces trust. We confirm this relationship is causal: Manipulating explainability visualizations to control comprehension, bias perception, and trust, we show that visualization design can significantly (p < 0.001) increase comprehension, increase perceived bias, and reduce trust. Conversely, reducing perceived model bias, either by improving model fairness or by adjusting visualization design, significantly increases trust even when comprehension remains high. Our work advances understanding of how comprehension affects trust and systematically investigates visualization's role in facilitating responsible ML applications.

Accounting for individual differences can improve the effectiveness of visualization design. While the role of visual attention in visualization interpretation is well recognized, existing work often overlooks how this behavior varies based on visual literacy levels. Based on data from a 235-participant user study covering three visualization tests (mini-VLAT, CALVI, and SGL), we show that distinct attention patterns in visual data exploration can correlate with participants' literacy levels: While experts (high-scorers) generally show a strong attentional focus, novices (low-scorers) focus less and explore more. We then propose two computational models leveraging these insights: Lit2Sal -- a novel visual saliency model that predicts observer attention given their visualization literacy level, and Sal2Lit -- a model to predict visual literacy from human visual attention data. Our quantitative and qualitative evaluation demonstrates that Lit2Sal outperforms state-of-the-art saliency models with literacy-aware considerations. Sal2Lit predicts literacy with 86% accuracy using a single attention map, providing a time-efficient supplement to literacy assessment that only takes less than a minute. Taken together, our unique approach to consider individual differences in salience models and visual attention in literacy assessments paves the way for new directions in personalized visual data communication to enhance understanding.