Taking Data out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information

Julia Hanson, Miranda Wei, Sophie Veys, Matthew Kugler, Lior Strahilevitz, Blase Ur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data brokers and advertisers increasingly collect data in one context and use it in another. When users encounter a misuse of their data, do they subsequently disclose less information? We report on human-subjects experiments with 25 in-person and 280 online participants. First, participants provided personal information amidst distractor questions. A week later, while participants completed another survey, they received either a robotext or online banner ad seemingly unrelated to the study. Half of the participants received an ad containing their name, partner's name, preferred cuisine, and location; others received a generic ad. We measured how many of 43 potentially invasive questions participants subsequently chose to answer. Participants reacted negatively to the personalized ad, yet answered nearly all invasive questions accurately. We unpack our results relative to the privacy paradox, contextual integrity, and power dynamics in crowdworker platforms.

Original languageEnglish (US)
Title of host publicationCHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450367080
DOIs
StatePublished - Apr 21 2020
Event2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 - Honolulu, United States
Duration: Apr 25 2020Apr 30 2020

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
CountryUnited States
CityHonolulu
Period4/25/204/30/20

Keywords

  • creepy
  • hyper-personalization
  • targeted advertising
  • user study

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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    Hanson, J., Wei, M., Veys, S., Kugler, M., Strahilevitz, L., & Ur, B. (2020). Taking Data out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information. In CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems [3376415] (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376415