photo of Jane smiling
(Photo credit: Jeffrey M. Smith)

As a Human-Computer Interaction researcher, I design and build social computing systems (e.g., social media, workplace software) grounded in affirmative consent, an idea that a person or a system must ask for, and earn, enthusiastic approval before interacting with an individual. Consent is an important concept to define for software and policy design to tackle socio-technical problems—these span companies’ massive data collection and inferences about users, especially with the rise of AI, to harmful interpersonal dynamics mediated by technology that disproportionately impact marginalized groups.

My research advanced the theory of affirmative consent by applying its properties (affirmative consent is voluntary, informed, revertible, specific, and unburdensome) to the design of social computing systems (CHI 2021). Based on such theory, I create systems and interfaces, and evaluate them by conducting field studies and experiments. Concretely, my directions include:

  • theoretizing and building social platforms that center users' consent boundaries (CHI 2021, CHI LBW 2024, in submission); e.g., Moa, a platform for enabling discussions among PhD students navigating challenging advising relationships—so that users have precise control over their posts' audience, instead of rigidly centering a particular space or network
  • developing data and algorithm interfaces that help users make privacy decisions closer to their affirmative consent (CHI 2023, CHI 2024)
  • rethinking social media’s business models—because it is fundamental to how companies obtain users' consent (ongoing)

I am a Ph.D. candidate at the University of Michigan School of Information and the Department of Computer Science & Engineering, advised by Professor Kentaro Toyama. My research has been recognized with a Meta Research PhD Fellowship (selected on my fourth try), University of Michigan Barbour Scholarship, EECS Rising Star, and two honorable mentions. I am also committed to practical impact—Moa, a platform I built to enable sensitive information sharing based on principles of affirmative consent, has attracted around 50 users so far and provided real-world help to PhD students navigating challenging PhD advising dynamics. I have also been invited by the Federal Trade Commission to present my research on designing advertisement privacy controls, which was previously covered by The Wall Street Journal.

I am on the academic job market for 2024-2025.





Selected First Author Publications

Full Papers
Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising
Jane Im, Ruiyi Wang, Weikun Lyu, Nick Cook, Hana Habib, Lorrie Cranor, Nikola Banovic, Florian Schaub
CHI 2023
Covered by The Wall Street Journal (The article is behind a paywall, but UMSI also wrote about it here.)
Invited by FTC to present to policymakers at PrivacyCon 2024

Yes: Affirmative Consent as a Theoretical Framework for Understanding and Imagining Social Platforms
Jane Im, Jill Dimond, Melody Berton, Una Lee, Katherine Mustelier, Mark Ackerman, Eric Gilbert
CHI 2021
Best Paper Honorable Mention
I want to give a shoutout to the incredible Una Lee. This work builds on and could not have existed without Una's impactful work on consentful technologies. Una introduced the term "consentful technology"—which inspired many people, including me.

Extended Abstracts
Understanding How to Design a Social Computing System That Helps PhD Students Collectively Navigate Mistreatment or Abuse in Advising Relationships
Jane Im, Kentaro Toyama
CHI 2024 Extended Abstract (Late-Breaking Work)
pdf - talk

Improving Advising Relationships Between PhD Students and Faculty in Human-Computer Interaction
Jane Im, Himanshu Zade, Steve Oney, Pamela Wisniewski, Kentaro Toyama
CHI 2024 Extended Abstract (Panel Proposal)

Selected Co-Authored Publications
I mentored the lead author for the following work.

"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for Personalization
Sumit Asthana, Jane Im, Zhe Chen, Nikola Banovic
CHI 2024
pdf

See all publications/projects



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