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

arXiv: https://arxiv.org/abs/2405.14034 · HTML: https://mlsanity.com/paper/2405.14034/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2024.

## Built on by (most cited first, 13 of 13)

- [Open Deep Search: Democratizing Search with Open-source Reasoning Agents](https://mlsanity.com/paper/2503.20201/) (2025)
- [News Source Citing Patterns in AI Search Systems](https://mlsanity.com/paper/2507.05301/) (2025)
- [Auditing Google's AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy](https://mlsanity.com/paper/2511.12920/) (2025)
- [Characterizing Web Search in The Age of Generative AI](https://mlsanity.com/paper/2510.11560/) (2025)
- [Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers](https://mlsanity.com/paper/2510.00361/) (2025)
- [Answer Bubbles: Information Exposure in AI-Mediated Search](https://mlsanity.com/paper/2603.16138/) (2026)
- [How Large Language Models Source Brand Reputation Across Languages and Markets](https://mlsanity.com/paper/2606.25787/) (2026)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediaries](https://mlsanity.com/paper/2603.20062/) (2026)
- [From Stochastic to Stable: Rank Stability and Structural Sufficiency in AI Visibility Measurement](https://mlsanity.com/paper/2607.10341/) (2026)
- [Understanding Generative AI-mediated User Engagement with Academic Library Resources](https://mlsanity.com/paper/2607.20328/) (2026)
- [Investigating the Impacts of Generative AI on Information Seeking](https://mlsanity.com/paper/2609.14638/) (2026)
- [What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)](https://mlsanity.com/paper/2610.15033/) (2026)

## Abstract (arXiv)

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.