Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots

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 awareness of the potential downsides of AI chatbot use, and to empower users to enhance, rather than diminish, human intelligence, to enrich, rather than compromise, daily life.

Publication Details

Journal
ArXiv.org
Published
2025-08-14
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots

ArXiv.org
Artificial Intelligence in Healthcare and Education
article

Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots

article en

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 awareness of the potential downsides of AI chatbot use, and to empower users to enhance, rather than diminish, human intelligence, to enrich, rather than compromise, daily life.

ArXiv.org
Peace, Justice and strong institutions
Openalex Percentile: Top 54%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.