Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots
Chengen Wang, Murat Kantarcioglu

TL;DR
This paper reviews the risks associated with individual use of AI chatbots like ChatGPT, discusses their limitations, and proposes mitigation strategies to reduce potential harms and enhance user awareness.
Contribution
It provides a comprehensive overview of risks, limitations, and mitigation strategies for individual users of LLM-based AI chatbots, filling a gap in user-focused research.
Findings
Identifies key risks such as hallucinations and biases
Proposes mitigation strategies for safer chatbot use
Highlights importance of user awareness and education
Abstract
As ChatGPT and other Large Language Model (LLM)-based AI chatbots become increasingly integrated into individuals' daily lives, important research questions arise. What concerns and risks do these systems pose for individual users? What potential harms might they cause, and how can these be mitigated? In this work, we review recent literature and reports, and conduct a comprehensive investigation into these questions. We begin by explaining how LLM-based AI chatbots work, providing essential background to help readers understand chatbots' inherent limitations. We then identify a range of risks associated with individual use of these chatbots, including hallucinations, intrinsic biases, sycophantic behavior, cognitive decline from overreliance, social isolation, and privacy leakage. Finally, we propose several key mitigation strategies to address these concerns. Our goal is to raise…
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