HOW TO PARTICIPATE

Zoom link: https://umich.zoom.us/j/91648623329
Meeting ID: 916 4862 3329
Passcode: misc2021

TITLE

Visually Encoding Personal Data for Vulnerable Populations

SPEAKER

Jaime Snyder, University of Washington

ABSTRACT

“How am I doing?” Personal informatics and self-tracking systems contribute to expectations that personal health and wellness questions like this can be answered with data. Visualizations play a pivotal role in many PI systems by making tracking data available to end users. As a result, conventions related to visually encoding are deeply implicated in explicit and implicit associations between self-knowledge and personal data. For vulnerable populations like those who self-track to manage serious mental illness (SMI), standardized approaches to visualizing data can introduce normative expectations and inappropriate behavioral targets. This talk will focus on a multi-phase design research project conducted in collaboration with individuals diagnosed with bipolar disorder, a chronic SMI characterized by difficult to predict mood swings and often managed through therapeutic self-tracking. The study provides a basis for discussing the influence of affect, visual conventions, and vernacular literacies on the interpretation and use of personal data. Speculative design concepts created through a grounded design process introduce alternatives to representing and presenting personal data beyond standard approaches to data visualization. I will use this work to highlight key questions about the creation and use of visual representations of data that motivate the work that we do in the Visualization Studies Research Studio, including:

• What characteristics of data do we making visible, why, and for whom? What motivates these choices and what values are reflected in these decisions?
• What are the mechanisms of visual encoding that surface certain things and obscure others? How are data made visible?
• And what are the implications of these design choices, especially in terms of communication, collaboration and coordination across individuals with distinctly different training, points of reference, and visual literacies?

BIO

Jaime Snyder (http://www.jaimesnyder.com/) is an Assistant Professor in the Information School at the University of Washington in Seattle, an Adjunct Assistant Professor in the Department of Human Centered Design and Engineering, and a Core Affiliate in the Data Science Studies Special Interest Group. Prior to completing a PhD in Information Science and Technology at the Syracuse University School of Information Studies, Snyder’s training and professional practice was centered in visual art, specifically site-specific experimental drawing and painting. She earned a BFA in Painting from Tyler School of Art and an MFA in Visual Art from Stanford University. Building on this strong foundation in the critical image-making, her work as an information scientist has focused on the creation and use of visual representations of data, information, and knowledge as a means of social interaction. At the University of Washington, she leads the Visualization Studies Research Studio where her team uses qualitative and design research methods to engage in contexts where people from very different backgrounds with diverse types of expertise use visual materials to coordinate and collaborate. Her work has appeared in top HCI and information science venues such as ACM proceedings of CHI, CSCW, ASSETS, and DIS; ACM TOCHI; JASIST; Computers in Human Behavior; and Human-Computer Interaction. Snyder’s research has received recognitions from ACM SIGCHI and CSCW for contributions to diversity, equity, and inclusion and has been funded by UW’s Royalty Research Fund (RRF), Group Health Foundation, and an NSF CAREER award, among others.

This event is co-organized by ESC and Michigan Interactive and Social Computing (MISC).