Abstract
Abstract Recently, there has been a growing trend in using large language models (LLMs) to develop diverse applications suitable for a wide range of tasks. These tasks range from solving programming bugs to helping teach elementary school students how to enhance their writing. Even with all these beneficial use cases, researchers worry about the potential bias these tools could produce and their effect on society. In this research, we compared responses that resulted from prompting two chatbots, namely OpenAI ChatGPT and Google Bard, about the issue of gene editing. Twelve prompts that are part of two subgroups were used to generate responses (text) about the issue of gene editing when the political affiliation (Democrat, Republican, and Communist) or geographical areas (United States, China, and Europe) of the prompter is provided. The Twelve responses were then analyzed semantically using three dictionary-based tools, i.e., Linguistic Inquiry and Word Count, the Moral Foundation Theory and Biblical Ethics dictionary, and Google’s Perspective API, to test and analyze the semantic and linguistic differences (measured via the Mann–Whitney U test) in the responses returned from the two chatbots. The results suggest that there are semantic and linguistic differences in responses per chatbots and prompts.