Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authority

TL;DR

This study audits popular generative AI search engines to assess biases in sentiment, source authority, and response quality, highlighting the importance of critical evaluation of AI-generated information for public decision-making.

Contribution

It provides a systematic analysis of biases and source reliability in responses from ChatGPT, Bing Chat, and Perplexity, revealing uneven source quality and potential biases.

Findings

Sentiment bias varies with queries and topics

Sources are heavily biased towards news and media outlets

Response quality and source reliability are inconsistent

Abstract

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.