Link to Paper: arxiv.org/abs/2508.08383
This work, with Gordon Kindlmann and @kalealex.bsky.social, marks my first paper as a PhD student. I’d love to hear your thoughts! n/n

Furthermore, we unpack broader implications of reframing visualization as a mechanism for data disclosure such as in authoring tools where designers like Fred are recommended possible visualization designs based on their disclosure goals. 8/n

From this analysis, we present a taxonomy of 13 data operations, what we dub disclosure tactics. These tactics give us a shared vocabulary to think about design tradeoffs in terms of disclosure, whether it is to protect privacy, persuade viewers, or simplify complex patterns. 7/n

We analyzed 425 examples of visualization techniques to study how designers made choices about information disclosure - about what information is hidden and what information is revealed in a chart. 6/n

In our new paper, we ask: Can these decisions be made more deliberate? Can we name and describe the operations that shape what a visualization reveals, distorts, or hides? 5/n

Without clear guidance from existing tools, he’s left to experiment until something “looks right,” hoping it meets both goals. 4/n

For example, imagine a bio-statistician Fred who works at a university medical center. He’s tasked with revealing clusters in patient data to an industry partner, while protecting patient privacy. 3/n

Visualizing real-world data means making tradeoffs: Datasets can be messy, high-dimensional, or contain sensitive information. Designers are constantly making judgment calls about what to show and what to leave out.  2/n