Privacy Strategies for Conversational AI and their Influence on Users' Perceptions and Decision-Making

Conversational AI (CAI) systems are on the rise and have been widely adopted in homes, cars and public spaces. Yet, people report privacy concerns and mistrust in these systems. Current data protection regulations ask providers to communicate data practices transparently and provide users with options to control their data. However, even if users are given control, their decisions can be subject to heuristics and biases leaving people frustrated and regretful. Based on the idea of conversational privacy and debiasing, we design three privacy strategies for CAI that allow people to have their data deleted while at the same time promoting rational decision-making. We conduct a user study to test our strategies in two widespread scenarios using a text-based CAI system and evaluate their impact on peoples’ privacy perception, usability and attitude-behaviour alignment. We find that our strategies can significantly change people’s behaviour, but do not influence peoples’ privacy perception. Finally, we discuss evaluation metrics and future research directions to investigate privacy controls in Conversational AI systems.