The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots

2026-07-07

AI Summary

This study investigates how safety and privacy control mechanisms in generative AI chatbots influence users’ willingness to disclose personal information and their trust in emotionally supportive interactions. Employing a mixed-methods approach combining scenario-based simulations and surveys (N=354), the research finds that intuitive user controls—such as data deletion features—significantly enhance emotional engagement and perceived privacy protection. In contrast, technically sophisticated mechanisms like on-device processing fail to foster trust due to low comprehensibility. The findings reveal widespread user skepticism regarding the actual efficacy of privacy controls and underscore the critical importance of designing transparent, user-friendly interfaces to maximize the effectiveness of AI-driven emotional support.

Abstract

Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of these controls influences users' attitudes toward emotionally sensitive disclosure remains understudied. We conducted a mixed-methods vignette study with 354 U.S. participants to examine how S&P controls influence users' willingness to engage with generative AI chatbots for emotional support, their perceptions of how protected they are when using these systems, and their perceptions of how effective the chatbots are for providing support. Controls enabling deletion of disclosures had the largest positive impact: these offerings outperformed technically sophisticated controls such as local-only processing and model training opt-outs, where participants expressed difficulty understanding the underlying mechanisms. Yet trust remains fragile, and participants often doubted S&P controls would function as promised. We conclude with actionable recommendations informed by our results to bridge users' comprehension gaps, build credible assurances, and properly calibrate barriers for users in distress.

Problem

• security and privacy controls
• emotional engagement
• generative AI chatbots
• user trust
• emotionally sensitive disclosure

Innovation

• generative AI chatbots
• security and privacy controls
• emotional engagement
• user trust
• vignette study