This article discusses the role of text in visualizations through a study. The focus is on how annotations can shape perceptions of bias and influence predictions. The study uses various charts comparing two entities, ‘Blue’ and ‘Green’, in different scenarios. The authors demonstrate how different annotations can lead to different interpretations of the same data, potentially causing perceived bias towards one entity or another. The findings highlight the importance of careful text usage in data visualization to avoid misleading interpretations.
Publication date: 9 Jan 2024
Project Page: Not Provided
Paper: https://arxiv.org/pdf/2401.04052
