Alvitta Ottley

Should you place three pie charts side by side, or should you avoid pie charts altogether? Publicly available visualization style guides offer contradictory answers to such questions. Despite their growing influence on how people encounter data, these guides are seldom studied as a collective phenomenon. Addressing this gap, this paper presents the first systematic analysis of 53 publicly accessible visualization style guides from diverse domains, including journalism, government, non-profit, corporate, and academic sectors. We build a standardized corpus, conduct a multi-method analysis that reveals both consensus and contradiction, and develop a companion Guidelines Explorer to support transparency and future use. This work sheds light on organizational visualization design norms and provides a foundation for future work that helps bridge the gap between academic and industry practices. In doing so, we help reframe style guides as sociotechnical artifacts that encode values as much as design rules.

Plea bargains are commonly used in the criminal justice system, where they can offer potential benefits to both the prosecution and the defendant. However, research has shown that defendants often engage in poor decision-making, such as accepting the plea even when the trial sentence is likely to be less severe. While previous studies have shown some evidence that uncertainty visualizations can improve decision-making, there is a lack of research on their effectiveness in domain-specific tasks like plea bargain decision-making. In this work, we conduct a series of experiments to explore whether the presence and format of uncertainty impact plea bargain decisions, taking into account time pressure and individual differences. Our findings reveal that these factors can have a significant impact on plea bargain decisions. We also show evidence that communicating uncertainty in the form of text can elicit more optimal decisions under time-pressure conditions.

Despite the importance of viewers’ trust in data visualization, there is a lack of research on the viewers’ own perspective on their trust. In addition, much of the research on trust remains relatively theoretical and inaccessible for designers. This work aims to address this gap by conducting a qualitative study to explore how viewers perceive different data visualizations and how their perceptions impact their trust. Three dominant themes emerged from the data. First, users appeared to be consistent, listing similar rationale for their trust across different stimuli. Second, there were diverse opinions about what factors were most important to trust perception and about why the factors matter. Third, despite this disagreement, there were important trends to the factors that users reported as impactful. Finally, we leverage these themes to give specific and actionable guidelines for visualization designers to make more trustworthy visualizations.

The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI’s lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people’s comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants’ Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.