Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliable
This commentary highlights a critical issue for users and researchers in information retrieval, but it is incremental as it discusses known challenges without new solutions.
The paper tackles the problem of search engines integrating generative AI, which leads to factual inconsistencies, biases, and reduced transparency, potentially making search less reliable by blurring information provenance and increasing errors.
In this commentary, we discuss the evolving nature of search engines, as they begin to generate, index, and distribute content created by generative artificial intelligence (GenAI). Our discussion highlights challenges in the early stages of GenAI integration, particularly around factual inconsistencies and biases. We discuss how output from GenAI carries an unwarranted sense of credibility, while decreasing transparency and sourcing ability. Furthermore, search engines are already answering queries with error-laden, generated content, further blurring the provenance of information and impacting the integrity of the information ecosystem. We argue how all these factors could reduce the reliability of search engines. Finally, we summarize some of the active research directions and open questions.