Authority Signals in Claude AI Health Citations: A Descriptive Analysis Using the Authority Signals Framework 文章

ArXiv CS.AI2026-05-26NEWSen作者: Erin T. Jacques (York College, CUNY), Erela Datuowei (Teachers College, Columbia University), Elizabeth Quaye (York College, CUNY), Corey H. Basch (William Paterson University), Arijit Chatterjee (York College, CUNY), Juanita Davis (York College, CUNY)

摘要

arXiv:2605.23921v1 Announce Type: cross Abstract: This study seeks to determine the authority signals used by Anthropic's Claude AI in its presentation of sources when answering consumer health questions. While there exists a great deal of discourse around the quality of health citations that LLMs produce, there is limited information on the integrity of the sources the citations originate from, and to what extent the sources are, from what health professionals would consider, credible sources. This descriptive cross-sectional study used data from HealthSearchQA, which contains 3,172 consumer health questions curated by Google Research. After exclusions, a final dataset of 3,075 questions yielding 10,038 citations was analyzed. The Authority Signals Framework (Jacques et al., 2026) was applied to examine 10 authority signals across four domains for a disproportionate stratified sample of 542 sources. Established institutional sources accounted for 97.8% of all citations (n = 9,818).