November 10, 2026, 15:00-16:30

Facts & All the Feels:
Data Visualization Through the Affective Lens

IEEE VIS 2026, Boston, Massachusetts

About the Panel

Although data visualization has long been associated with objectivity and neutrality, we are reckoning with the impact of design choices on how data is not only understood, but also how it is felt. In particular, visualizations used in high-stakes public advocacy contexts (e.g., climate change, public health, social-civic issues, and science communication) are often designed to emotionally engage as well as to inform. Designers must strike a balance between these goals, as they often also shape attitudes and behavioural intentions.

Affective visualization research elucidates the role of emotions, individual characteristics, and sociocultural contexts in how information is processed and acted upon. The rhetorical nature of visualization design and subjective audience experience requires us to examine ethical questions that go beyond merely avoiding the design of misleading visualizations; we must also consider how the data visualization pipeline, from data collection and processing to design and dissemination, carry emotional weight and influence trust and receptivity amongst diverse audiences.

If visualizations are meant to engage and inform public audiences, what does responsible engagement actually look like?

When is it appropriate or not to design for emotion in visualization, and to what end?

What responsibility do visualization designers and media platforms have when visualizations circulate through our complex media ecosystem to influence public discourse?

This panel will draw the visualization community into discussion about how we should account for, and can more effectively measure, the impact of emotional techniques applied in visualizations for public communication, with the aim of guiding us toward responsible practices.

Panelists

Crystal Lee, Assistant Professor, MIT
Crystal is an Assistant Professor at MIT. She is also a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University, where she co-leads the Ethical Tech Working Group. Her research sits at the intersection of human-computer interaction, science and technology studies, and disability studies. Her forthcoming book, The Social Life of Data Visualization, traces how charts became one of the 20th century's most effective instruments of scientific, corporate, and bureaucratic decision-making. She also researches how people collect, organize, and distribute information on the internet, including empirical studies of platform design and internet governance.

Narges Mayhar, Senior Lecturer,
City St George's, U. of London
Narges’s research falls at the intersection of Visualization, HCI, Social Computing, and Design with the goal of augmenting people’s abilities to solve complex problems. She designs, develops, and evaluates novel social computing and visualization techniques that help people (both experts and non-experts) make better decisions. More recently, she has focused on exploring new strategies for scaling and diversifying public engagement in massive decision-making processes related to civic issues.

Jonathan Schwabish, Founder, PolicyViz
is founder of PolicyViz, a data visualization and presentation skills firm, and a Senior Fellow in the Tax and Income Supports Division at the not-for-profit research institution, Urban Institute, where he also leads the Do No Harm Project, which outlines ways to make data work more equitable, inclusive, and accessible. He has written five books on data visualization, including the upcoming book,
Thoughtful Data.

Wen Xu, PhD Researcher, MIT
Wen is a 2nd-year C.S. Ph.D. candidate in the Khoury Data Visualization Lab at Northeastern University, advised by Dr. Lace Padilla. She obtained a BSc in Psychology with a minor in Computer Science from Middlebury College. Her research focuses on the intersection between cognitive psychology and data visualization. She studies how visualization design shapes how viewers interpret and make decisions with data, and is particularly interested in how viewers' expectations and preconceptions play a role in this process.