Adjacency Risk Scoring

Adjacency Risk Scoring Framework

Adjacency Risk Scoring answers a simple question: how likely is it that an ad will show up next to garbage? We score the probability and severity of advertiser exposure to low-quality AI content.

The problem is straightforward — programmatic advertising puts ads where algorithms decide, and those algorithms don't reliably distinguish quality content from AI-generated junk. As AI content scales, the amount of bad inventory grows faster than brand safety tools can flag it.

Scoring Dimensions

The Adjacency Risk Score is a composite assessment across four primary dimensions, each contributing to an overall risk classification.

Content Environment Quality

What does the content around the ad look like? We evaluate originality, editorial standards, factual reliability, and the ratio of actual content to monetization elements. Pages stuffed with thin AI content and aggressive ad layouts score higher risk. Pages with original reporting and balanced monetization score lower.

Source Integrity

Is the publisher real? Source integrity checks whether the entity behind the content has a verifiable identity and editorial history, whether their content production patterns look human-scale or AI-automated, and whether related domains and accounts trace back to a single low-quality operation hiding behind multiple facades.

Audience Context

How did the user get here? Someone who typed a search query and clicked a result is in a different state of mind than someone who landed via a misleading recommendation widget. Users who arrive through deceptive pathways are frustrated, not receptive — and that's bad context for advertising. This dimension also flags when the likely audience includes vulnerable groups like children or people seeking health/financial guidance.

Monetization Pattern

Is the content the point, or is the advertising the point? Environments where monetization is the entire reason the content exists — rather than a revenue stream supporting genuine work — present higher risk. We look at ad-to-content ratios, affiliate link density, traffic arbitrage patterns, and impression-inflating tactics like ad refresh, excessive pagination, and auto-play.

Platform-Specific Considerations

Adjacency looks different on every platform.

On the open web, we can directly analyze page content and ad placement mapping. Both the immediate page and the broader site context matter.

On video platforms, adjacency includes both the video where ads run (pre-roll, mid-roll) and the recommendation sidebar. An ad in a quality video surrounded by AI-generated recommendation suggestions carries risk the video alone wouldn't suggest.

On social media, adjacency is defined by the feed. Ads appear in algorithmically assembled streams, so adjacency risk is partly a function of how much AI-generated content the platform's own recommendation system surfaces.

Confidence Levels

Every score includes a confidence level.

High confidence: Multiple signal categories agree, the source has enough history for statistical analysis, and we have direct access to analyze the content environment.

Moderate confidence: Some signals are limited, indicators are mixed, or access constraints reduce analysis depth.

Low confidence: Sparse signals, novel content types, or platform limitations prevent meaningful analysis. Low-confidence scores come with an explicit recommendation to review further before acting on them.

Integration with Advertiser Workflows

Adjacency Risk Scores plug into existing brand safety workflows — as pre-bid signals for programmatic buying, post-bid verification for campaign monitoring, or strategic inputs for inclusion/exclusion list management. The scoring framework is compatible with standard brand safety taxonomies while extending coverage to AI-specific risks those taxonomies don't yet address.

Continuous Calibration

The model is continuously calibrated through human review outcomes, advertiser feedback, and evolving AI content patterns. Signal weights and scoring thresholds are reviewed quarterly and adjusted when shifts in the landscape change the relationship between what we measure and actual advertiser risk.