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

Honorable Mention
Privacy by Design & User ControlPrivacy Perception & Decision-MakingDark Patterns RecognitionConsumers & ShoppersAmazon Mechanical Turk Workers

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.

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https://hci.top/en/papers/chi/32048/2020

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DOI: https://doi.org/10.1145/3313831.3376415
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Paper Snapshot

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Source
CHI
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Year
2020
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Award
Honorable Mention
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Authors
6 authors
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Subtopics
Privacy by Design & User Control, Privacy Perception & Decision-Making, Dark Patterns Recognition
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Professions
Consumers & Shoppers, Amazon Mechanical Turk Workers
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Content Status
Abstract only
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