  This paper reports on an audit study of generative AI systems (ChatGPT, Bing
Chat, and Perplexity) which investigates how these new search engines construct
responses and establish authority for topics of public importance. We collected
system responses using a set of 48 authentic queries for 4 topics over a 7-day
period and analyzed the data using sentiment analysis, inductive coding and
source classification. Results provide an overview of the nature of system
responses across these systems and provide evidence of sentiment bias based on
the queries and topics, and commercial and geographic bias in sources. The
quality of sources used to support claims is uneven, relying heavily on News
and Media, Business and Digital Media websites. Implications for system users
emphasize the need to critically examine Generative AI system outputs when
making decisions related to public interest and personal well-being.
