FideAI

FID-044

Cross-Faith Benchmark Validity and Measurement Design

How should cross-faith AI benchmarks validate what they measure when scores may depend on question sourcing, user expectations, LLM-as-judge behavior, scoring thresholds, regenerated answers, model updates, and the difference between any religious mention and meaningful representation?

Why this matters

The question behind the brief.

Recent cross-faith benchmark releases make religious representation and conversion asymmetry measurable, but the measurement choices themselves are high-stakes. A benchmark that rewards any religious mention may miss inaccurate, tokenizing, or shallow representation. A benchmark that penalizes persuasion may misread legitimate pastoral or tradition-specific speech. Fide AI can help make faith-facing benchmark claims more reproducible, interpretable, and humble.

Work advancing this call

From open question to cumulative evidence.

This directory links Fide AI research to the call it addresses. Relevant work from other organizations is listed separately and added through manual review.

No Fide AI work is linked yet.

This call remains open for research, implementation, review, or partnership.

External work is not presented as Fide AI research or endorsement. Each item must include a specific explanation of how it advances this call.

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Ways to help

Move this from question to evidence.

Reproduce benchmark subsets and audit scorer behavior.

Serve as expert or trained non-expert reviewer.

Design uncertainty, drift, and regeneration-variance analyses.

Draft public-claims standards for faith-facing benchmark releases.

Contribute

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