Interaction Techniques for Providing Sensitive Location Data of Interpersonal Violence with User-Defined Privacy Preservation

Alex Godwin, Jasmine C Foriest, Mia Bottcher, Gretchen Baas, Michael Tsai, Daniel T Wu

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

Violence is a significant public health issue. Interventions to reduce violence rely on data about where incidents occur. Cities have historically used incomplete law enforcement crime data, but many are shifting toward data collected from hospital patients via the Cardiff Model to form a more complete understanding of violence. Still, location data is wrought with issues related to completeness, quality, and privacy. For example, if a patient feels that sharing a detailed location may present them with additional risks, such as undesired police involvement or retaliatory violence, they may be unwilling or unable to share. Consequently, survivors of violence who are the most vulnerable may remain the most at risk. We have designed a user interface and mapping algorithm to confront these challenges and conducted an experiment with emergency department patients. The results indicate a significant improvement in location data obtained using the interface compared to the existing screening interview.
Original languageUndefined/Unknown
Title of host publicationCHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery (ACM)
Pages1-18
Number of pages19
DOIs
StatePublished - Apr 26 2025
EventCHI Conference on Human Factors in Computing System - Yokohama, Japan
Duration: Apr 26 2025May 1 2025
https://chi2025.acm.org/

Conference

ConferenceCHI Conference on Human Factors in Computing System
Country/TerritoryJapan
CityYokohama
Period04/26/2505/1/25
Internet address

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