Publication
Title
The risks of using ChatGPT to obtain common safety-related information and advice
Author
Abstract
ChatGPT is a highly advanced AI language model that has gained widespread popularity. It is trained to understand and generate human language and is used in various applications, including automated customer service, chatbots, and content generation. While it has the potential to offer many benefits, there are also concerns about its potential for misuse, particularly in relation to providing inappropriate or harmful safety-related information. To explore ChatGPT's (specifically version 3.5) capabilities in providing safety-related advice, a multidisciplinary consortium of experts was formed to analyse nine cases across different safety domains: using mobile phones while driving, supervising children around water, crowd management guidelines, precautions to prevent falls in older people, air pollution when exercising, intervening when a colleague is distressed, managing job demands to prevent burnout, protecting personal data in fitness apps, and fatigue when operating heavy machinery. The experts concluded that there is potential for significant risks when using ChatGPT as a source of information and advice for safety-related issues. ChatGPT provided incorrect or potentially harmful statements and emphasised individual responsibility, potentially leading to ecological fallacy. The study highlights the need for caution when using ChatGPT for safety-related information and expert verification, as well as the need for ethical considerations and safeguards to ensure users understand the limitations and receive appropriate advice, especially in low-and middle-income countries. The results of this investigation serve as a reminder that while AI technology continues to advance, caution must be exercised to ensure that its applications do not pose a threat to public safety.
Language
English
Source (journal)
Safety science. - Amsterdam, 1991, currens
Publication
Amsterdam : 2023
ISSN
0925-7535
DOI
10.1016/J.SSCI.2023.106244
Volume/pages
167 (2023) , p. 1-22
Article Reference
106244
ISI
001051970000001
Medium
E-only publicatie
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 02.10.2023
Last edited 25.04.2024
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