Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling

Abstract:Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user views (impressions) that went to content violating a given policy on a given day. Accurate prevalence measurement is challenging because violations are often rare and human labeling is costly, making frequent, platform-representative studies slow. We present a design-based measurement system that (i) draws daily probability samples from the impression stream using ML-assisted weights to concentrate label budget on high-exposure and high-risk content while preserving unbiasedness, (ii) labels sampled items with a multimodal LLM governed by policy prompts and gold-set validation, and (iii) produces design-consistent prevalence estimates with confidence intervals and dashboard drilldowns. A key design goal is one global sample with many pivots: the same daily sample supports prevalence by surface, viewer geography, content age, and other segments through post-stratified estimation. We describe the statistical estimators, variance and confidence interval construction, label-quality monitoring, and an engineering workflow that makes the system configurable across policies.

Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)

arXiv:2602.18518 [cs.LG]

(or arXiv:2602.18518v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.18518 Focus to learn more arXiv-issued DOI via DataCite

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