Agentic- and AI- driven strategies are quickly reshaping how content and ads are created, served, and consumed. With this, a new marketer expectation has emerged: explainability.
Marketers are no longer satisfied with simply seeing into their supply chains and having campaigns “optimized” on their behalf. They want to understand why their media performs the way it does and how the systems driving those decisions can be trusted.
The Industry’s Buzzword Problem: What Generalized “AI Slop” Protection Misses
“AI slop” has become shorthand for synthetic, repetitive, and low-quality content flooding digital media. It’s important to distinguish this from invalid traffic (IVT), which measures the legitimacy of the interaction itself versus the quality of the content being served. While tools that claim to “stop AI slop” promise quick fixes, they’re often opaque in providing explanations for what triggered the detection and why the tool blocked it.
HUMAN’s recent Iris Report: Marketers’ Perceptions of Brand Safety and Viewability Tools found that 78% of senior marketing leaders say they are open to or actively exploring changing their brand safety or viewability tool, and a top pain-point cited was a “lack of transparency and actionable reporting, especially in fast-moving environments.” The takeaway is clear: the problem is not automation, it is opacity. What matters is whether ad fraud vendors can connect detection to decisioning, giving platforms and advertisers the context to direct spend toward verified, high-quality media.
Agencies and platforms should be asking tougher questions. Are these solutions simply blocking what looks AI generated, or are they actually explaining why certain signals matter and how those insights should guide media investment?
Beyond Buzzwords: The Importance of a Nuanced Approach
At HUMAN, detecting fraudulent behavior has always been core to our mission. Long before AI slop became part of the industry vocabulary, our detection systems were identifying and classifying automated, manipulated, and low-quality behaviors at massive scale, verifying more than 20 trillion digital interactions weekly across 3 billion unique devices.
As the landscape has shifted, we’ve expanded our explainability approach to surface low-quality inventory signals such as AI-generated traffic, templated low-quality sites, and over-monetized apps, using the same underlying principles that guide our analysis of IVT and other forms of invalid behavior.
Fighting ad fraud is not a “set-it-and-forget-it” problem. The signals evolve, and so must the detection. That’s why our platform was built to adapt. By combining deterministic indicators with adaptive machine-learning models, we focus on uncovering the underlying behaviors and motivations behind anomalies. Whether it’s a headless crawler or a low-quality site/app activity, we identify the why behind these patterns, not just flag them as bad.
The Explainability Imperative
As AI becomes embedded in every layer of advertising, from creative generation to media optimization, explainability isn’t optional. It’s the mechanism that turns automation from a risk into a reliability signal.
Our approach brings explainability to inventory quality at scale:
- Deterministic Transparency: Every classification and enforcement decision is driven by clear, observable indicators.
- Granular Insight: Each domain or app is evaluated across multiple quality signals, surfacing why it was flagged rather than simply labeling it as “bad.”
- Unified Context: By integrating these insights across channels, we create a cohesive view of inventory quality whether the interaction originated on the open web, within an app, or through automated browsing.
This shift turns supply evaluation from static compliance to explainable intelligence, allowing platforms and marketers to see that quality controls are working with supporting evidence.
Reframing AI Slop as an Explainability Challenge
Previous forecasts have suggested that 90% of all web content could be AI generated by 2026. The real question today isn’t “was it made with AI,” but “is it actually worthwhile?” There’s nothing wrong with high-quality content that uses AI as part of the creative process.
The issue is the flood of low-value material that adds no substance or engagement. Slop is still slop. It’s just easier to produce at scale now that AI can do it faster. A repetitive content structure could be AI-generated junk, or simply a publisher’s CMS template.
Without explainability, these nuances are lost, which in turn could lead to overblocking, wasted impressions, and lost revenue. MFA detection and enforcement are no longer just about filtering “bad” sites. They’re about establishing evidence-based transparency. This is the ability to show exactly which behaviors degrade user experience or advertiser ROI.
HUMAN’s objective, indicator-based framework identifies low-quality signals, and these indicators are surfaced, not hidden. Every decision is explainable and customizable, enabling buyers and sellers to make informed, trust-based choices.
From Transparent to Trusted
The future of inventory integrity isn’t just about filtering out the noise; it’s about understanding its source. Transparency showed us what was happening. Explainability shows us why.
The path forward for the ad tech ecosystem is clear:
- Treat every automated signal as an opportunity for insight.
- Replace opaque decisioning with explainable intelligence.
- Build a supply strategy grounded in evidence, not assumptions.
In a world saturated with AI-driven noise and “slop,” clarity is the ultimate differentiator. At HUMAN, we believe explainability isn’t just a safeguard, it’s how trust scales. Connect with your HUMAN team today to learn how agentic activity and AI slop might be affecting your media strategies. Together, we can turn opaque supply into transparent, high-performance channels where media dollars work harder.