Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling
arXiv:2602.18518v1 Announce Type: new 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 surfac
arXiv:2602.18518v1 Announce Type: new 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.
Executive Summary
This article proposes a novel approach to measuring the prevalence of policy-violating content using machine learning-assisted sampling and large language model labeling. The system aims to provide accurate and unbiased estimates of the fraction of user views that violate a given policy on a given day. By leveraging ML-assisted weights and multimodal LLM labeling, the system can efficiently concentrate label budget on high-exposure and high-risk content while preserving unbiasedness. The design enables the production of design-consistent prevalence estimates with confidence intervals and supports various segments through post-stratified estimation.
Key Points
- ▸ ML-assisted sampling for concentrating label budget on high-exposure and high-risk content
- ▸ Multimodal LLM labeling governed by policy prompts and gold-set validation
- ▸ Design-consistent prevalence estimates with confidence intervals and dashboard drilldowns
Merits
Efficient Labeling
The system's use of ML-assisted weights and multimodal LLM labeling enables efficient labeling of content, reducing the need for costly human labeling.
Demerits
Complexity
The system's design and implementation may be complex, requiring significant expertise in machine learning, statistics, and software engineering.
Expert Commentary
The proposed system represents a significant advancement in the measurement of policy-violating content. By leveraging machine learning and large language models, the system can efficiently and accurately estimate the prevalence of such content. The system's design, which enables the production of design-consistent prevalence estimates with confidence intervals, is particularly noteworthy. However, the system's complexity may pose challenges for implementation and maintenance, highlighting the need for careful consideration of the trade-offs between accuracy, efficiency, and complexity.
Recommendations
- ✓ Further research is needed to evaluate the system's performance in real-world settings and to explore potential applications in other domains.
- ✓ Content safety teams should consider implementing the system as part of their content moderation strategies, with careful attention to the system's limitations and potential biases.
