An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors
arxiv(2023)
摘要
Managing privacy to reach privacy goals is challenging, as evidenced by the
privacy attitude-behavior gap. Mitigating this discrepancy requires solutions
that account for both system opaqueness and users' hesitations in testing
different privacy settings due to fears of unintended data exposure. We
introduce an empathy-based approach that allows users to experience how privacy
attributes may alter system outcomes in a risk-free sandbox environment from
the perspective of artificially generated personas. To generate realistic
personas, we introduce a novel pipeline that augments the outputs of large
language models (e.g., GPT-4) using few-shot learning, contextualization, and
chain of thoughts. Our empirical studies demonstrated the adequate quality of
generated personas and highlighted the changes in privacy-related applications
(e.g., online advertising) caused by different personas. Furthermore, users
demonstrated cognitive and emotional empathy towards the personas when
interacting with our sandbox. We offered design implications for downstream
applications in improving user privacy literacy.
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