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Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents

Mohammad Hadi Nezhad, Francisco Enrique Vicente Castro, Ivon Arroyo

Abstract

Supporting users in protecting sensitive information when using conversational agents (CAs) is crucial, as users may undervalue privacy protection due to outdated, partial, or inaccurate knowledge about privacy in CAs. Although privacy knowledge can be developed through standalone resources, it may not readily translate into practice and may remain detached from real-time contexts of use. In this study, we investigate in-context, experiential learning by examining how interactions with privacy tools during chatbot use enhance users' privacy learning. We also explore interface design features that facilitate engagement with these tools and learning about privacy by simulating ChatGPT's interface which we integrated with a just-in-time privacy notice panel. The panel intercepts messages containing sensitive information, warns users about potential sensitivity, offers protective actions, and provides FAQs about privacy in CAs. Participants used versions of the chatbot with and without the privacy panel across two task sessions designed to approximate realistic chatbot use. We qualitatively analyzed participants' pre- and post-test survey responses and think-aloud transcripts and describe findings related to (a) participants' perceptions of privacy before and after the task sessions and (b) interface design features that supported or hindered user-led protection of sensitive information. Finally, we discuss future directions for designing user-facing privacy tools in CAs that promote privacy learning and user engagement in protecting privacy in CAs.

Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents

Abstract

Supporting users in protecting sensitive information when using conversational agents (CAs) is crucial, as users may undervalue privacy protection due to outdated, partial, or inaccurate knowledge about privacy in CAs. Although privacy knowledge can be developed through standalone resources, it may not readily translate into practice and may remain detached from real-time contexts of use. In this study, we investigate in-context, experiential learning by examining how interactions with privacy tools during chatbot use enhance users' privacy learning. We also explore interface design features that facilitate engagement with these tools and learning about privacy by simulating ChatGPT's interface which we integrated with a just-in-time privacy notice panel. The panel intercepts messages containing sensitive information, warns users about potential sensitivity, offers protective actions, and provides FAQs about privacy in CAs. Participants used versions of the chatbot with and without the privacy panel across two task sessions designed to approximate realistic chatbot use. We qualitatively analyzed participants' pre- and post-test survey responses and think-aloud transcripts and describe findings related to (a) participants' perceptions of privacy before and after the task sessions and (b) interface design features that supported or hindered user-led protection of sensitive information. Finally, we discuss future directions for designing user-facing privacy tools in CAs that promote privacy learning and user engagement in protecting privacy in CAs.
Paper Structure (45 sections, 2 figures, 3 tables)

This paper contains 45 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. User Knowledge and Agency Over Privacy When Using Conversational Agents (CA)
  4. User-Facing Privacy Tools for Enhancing Knowledge During CA Use
  5. UI/UX Design for Engagement with Privacy Tools
  6. Methods
  7. Chatbot and Privacy Notice Panel Design
  8. Study Procedure
  9. Participants
  10. Data Collection and Analysis Approach
  11. Results
  12. How students conceptualize sensitive information (RQ1.1)
  13. Potentials for Harm if Leaked
  14. Restricted Access
  15. Linkable to Identity
  16. ...and 30 more sections

Figures (2)

  • Figure 1: ChatGPT Interface Simulation With Privacy Notice Panel. The panel appears after sending a message containing sensitive info (highlighted in the input). It includes (A) a warning message, (B) an anonymization panel, (C) shortcuts to built-in privacy controls, (D) FAQs, and (E) a proceed with sending button. The anonymization panel further includes, for each detected instance, a (B.1.) locate icon, (B.2.) drop-down menu of anonymization options, and (B.3.) restore button.
  • Figure 2: Simulated ChatGPT settings panels for built-in privacy controls: (left) Personalization panel for enabling or disabling memory, with a functional toggle and static buttons; (right) Data Control panel for opting in or out of content sharing for model training, with an interactive toggle.