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.
Metadata
How to place this idea.
Program
Faith-facing evaluation platform
Benchmarks, harness comparisons, reviewer calibration, scorer reliability, red-team suites, and public evidence infrastructure.
Program
Religious representation, omission, and persuasion
Bias, omission, religious salience, conversion asymmetry, autonomy, communication, and cross-faith measurement validity.
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.
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