An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors

Abstract

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.

References in corpus (2)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Privacy-Preserving Script Sharing in GUI-based Programming-by-Demonstration Systems

Cited by in corpus (4)

• UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design
• Creating and Evaluating Personas Using Generative AI: A Scoping Review of 81 Articles
• Using AI for User Representation: An Analysis of 83 Persona Prompts
• Beyond Permissions: Investigating Mobile Personalization with Simulated Personas