I’ve been wrestling with this for the last few months—we’re running campaigns across Russia and the US, and our in-house AI tools flag suspicious influencer accounts pretty consistently. But here’s the thing: we keep getting false positives on creators who are actually legit, especially when cultural context matters. A creator with inconsistent posting schedules in one market might be totally normal in another.
I started thinking about what it would actually look like to layer human expertise on top of the AI signals. Not to replace automation, but to calibrate it. We’ve been building playbooks based on real case data—patterns we’ve seen blow up before—and I’m finding that when we combine AI’s speed with actual expert validation from people who understand both markets, we catch the real fraud without burning bridges with authentic creators.
The problem is scaling this. We can’t manually validate every influencer for every campaign. So we’re experimenting with a tiered approach: let AI filter the obvious stuff, but for the borderline cases—the ones that matter for brand safety—we’re routing those to a network of people who actually know the local landscape.
I’m seeing brand safety as less about creating a perfect algorithm and more about having a repeatable process that doesn’t rely on luck. What does your team do when AI and local knowledge conflict on an influencer?
This is exactly what we’re pitching to clients now. The agencies I talk to are tired of black-box solutions—they want transparency on why an influencer is flagged, not just a score. We’ve started building this into our standard workflow: AI does the heavy lifting on basic fraud signals (fake followers, engagement anomalies, follower velocity), then our team validates the context. For bilingual markets, this is non-negotiable. We caught a campaign that would’ve blown up because an AI system didn’t understand that a certain creator’s engagement dip was seasonal, not fraudulent. Cost us credibility with a client until we could explain the nuance. Now we lead with that story—it’s actually a selling point. How are you structuring the hand-off between automation and expert review?
One tactical question: are you tracking false positives and false negatives separately in your playbooks? We started keeping clean data on what AI got wrong, and it’s become our most valuable asset for training new team members and refining alerts. The cases where AI missed fraud are especially important—those usually reveal patterns the algorithm wasn’t weighted to catch. Have you thought about making that feedback loop systematic?
From my side as a creator, I’ve definitely been flagged by automated systems that clearly didn’t get my content style. I had a major engagement drop during a campaign hiatus—totally planned, I was working on a product launch—and an agency ghosted me because their AI thought I was losing relevance. It sucked. What I appreciate is when brands or agencies actually ask creators questions before writing them off. Some of my best partnerships came from people who validated their concerns with me directly. Maybe that’s part of your playbook too? Like, the expert step isn’t just internal—it’s also reaching out and letting creators explain anomalies?
I’ve been thinking about this from a risk management angle. The real cost of fraud detection isn’t the false positives—it’s the false negatives. Getting one major fraud case wrong can tank a campaign’s performance metrics and damage the brand’s equity across platforms. So I’m less interested in a system that catches some fraud and more interested in one that’s calibrated for confidence. That means: what’s your precision and recall on this tiered approach you’re building? And how are you measuring it? Because without that data, you’re just trusting gut feel with extra steps. The expert validation piece is good, but it needs to be measured like any other control in your stack.