Abstract
As Large Language Models (LLMs) become increasingly integral to daily life, users are engaging with multiple LLM chatbots for various needs; however, prior research on LLM risks often remains lab-based or focuses on single LLMs like ChatGPT or singular risks like privacy. To gain a multi-risk, cross-chatbot understanding of user experiences, we analyze Reddit discussions around seven major LLM chatbots using the NIST AI Risk Management Framework. We find that user-reported risks are unevenly distributed and chatbot-specific: ChatGPT is associated with safety and fairness concerns, Gemini with privacy, and Claude with security and resilience. Less frequent risks, such as explainability and privacy, appear as user trade-offs, whereas prevalent risks like fairness are experienced as direct harms. Our findings underscore the need to operationalize chatbot-specific risk mitigation, moving beyond system-centered risk mitigation to human-centered interventions that align with users lived experiences.
Recommended Citation
L. Li et al., "Characterizing User-Reported Risks Across LLM Chatbots," Conference on Human Factors in Computing Systems Proceedings, article no. 602, Association for Computing Machinery (ACM), Apr 2026.
The definitive version is available at https://doi.org/10.1145/3772318.3791505
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
LLM chatbots; LLMs; trustworthy AI; user-reported risks
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Association for Computing Machinery (ACM), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
13 Apr 2026
