Debiasing Strategies for Conversational AI: Improving Privacy and Security Decision-Making

Abstract

With numerous conversational AI (CAI) systems being deployed in homes, cars, and public spaces, people are faced with an increasing number of privacy and security decisions. They need to decide which personal information to disclose and how their data can be processed by providers and developers. On the other hand, designers, developers, and integrators of conversational AI systems must consider users’ privacy and security during development and make appropriate choices. However, users as well as other actors in the CAI ecosystem can suffer from cognitive biases and other mental flaws in their decision-making resulting in adverse privacy and security choices. Debiasing strategies can help to mitigate these biases and improve decision-making. In this position paper, we establish a novel framework for categorizing debiasing strategies, show how existing privacy debiasing strategies can be adapted to the context of CAI, and assign them to relevant stakeholders of the CAI ecosystem. We highlight the unique possibilities of CAI to foster debiasing, discuss limitations of the strategies, and identify research challenges.