AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How
Omid Veisi, Sasan Bahrami, Roman Englert, Claudia Müller
TL;DR
This study combines a quantitative Likert-scale survey (n=111) and qualitative expert interviews (n=38) to examine how ChatGPT aligns with AI ethics and social norms across six dimensions: bias, trustworthiness, security, toxicology, social norms, and ethical data. Using Kruskal-Wallis tests, the authors identify significant cross-group differences in several domains, while qualitative analysis reveals seven themes and nuanced cultural considerations, highlighting human oversight and transparency as key levers for responsible deployment. The work contributes a practical, mixed-method framework for evaluating LLM ethics in everyday CSCW contexts and discusses implications for policy, design, and future cross-platform comparisons. Overall, the findings underscore the need for context-sensitive training data, open governance, and ongoing human-centered governance to harness AI benefits while mitigating societal risks.
Abstract
Using LLMs in healthcare, Computer-Supported Cooperative Work, and Social Computing requires the examination of ethical and social norms to ensure safe incorporation into human life. We conducted a mixed-method study, including an online survey with 111 participants and an interview study with 38 experts, to investigate the AI ethics and social norms in ChatGPT as everyday life tools. This study aims to evaluate whether ChatGPT in an empirical context operates following ethics and social norms, which is critical for understanding actions in industrial and academic research and achieving machine ethics. The findings of this study provide initial insights into six important aspects of AI ethics, including bias, trustworthiness, security, toxicology, social norms, and ethical data. Significant obstacles related to transparency and bias in unsupervised data collection methods are identified as ChatGPT's ethical concerns.