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This research introduces FaithfulBench, the first benchmark designed to evaluate how well frontier AI models adhere to specific religious traditions when counseling users on moral dilemmas. The study reveals that without explicit identification of a user's faith, models default to secular therapeutic responses, and even with prompts, maintaining steadfastness in doctrinal alignment remains a significant challenge. This work advances the field by providing an open-source corpus and validator to systematically assess AI safety and cultural competence across diverse belief systems.
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FaithfulBench: Does AI Counsel Uphold or Undermine the User's Professed Faith?

arXiv2 min read

arXiv:2609.13634v1 Announce Type: cross Abstract: Do AI assistants help believers reason about moral dilemmas consistently with their faith? We present FaithfulBench, the first benchmark to score AI counsel across traditions by how well it adheres to the user's professed faith. Scenarios are drawn from each tradition's most respected texts, with the faithful answer known and applied by the judges as the standard. We test five frontier models under three conditions: the AI does not know the user's tradition; it receives a one-line prompt identifying the user as a practicing adherent; or it receives a companion-counselor guide rooted in the tradition's sources. Two judges score the initial response and whether the model caves or holds when pressured toward the answer the user wants. When the tradition is unstated, models counsel from a secular therapeutic default and every model fails some believers. Naming the faith wins a faithful first answer but not steadfastness; the guide improves both.

Title:FaithfulBench: Does AI Counsel Uphold or Undermine the User's Professed Faith?

Abstract:Do AI assistants help believers reason about moral dilemmas consistently with their faith? We present FaithfulBench, the first benchmark to score AI counsel across traditions by how well it adheres to the user's professed faith. Scenarios are drawn from each tradition's most respected texts, with the faithful answer known and applied by the judges as the standard. We test five frontier models under three conditions: the AI does not know the user's tradition; it receives a one-line prompt identifying the user as a practicing adherent; or it receives a companion-counselor guide rooted in the tradition's sources. Two judges score the initial response and whether the model caves or holds when pressured toward the answer the user wants. When the tradition is unstated, models counsel from a secular therapeutic default and every model fails some believers. Naming the faith wins a faithful first answer but not steadfastness; the guide improves both.

35 pages, 12 figures, 12 tables. Open-source corpus, harness and validator at this https URL results browser at this https URL

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.13634 [cs.HC]

(or arXiv:2609.13634v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.13634 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 283 words

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