single detectors miss too much and flag too much. been recommending a ‘consensus of 3’ approach to my coaching clients but want to validate against what others are actually doing in production. if youre running multiple detectors as part of a real workflow: which combination, why those specific ones, and how do you handle disagreements between detectors
Running 3-detector consensus in production for 18 months. Combination matters more than the specific tools. Our criteria: 1) Different training data sources. Originality (commercial), Copyleaks (commercial with academic dataset), Proofademic (academic-leaning) cover different distributions. 2) At least one open source detector if you can swing it (we use BinocularsZero as an internal check). Open source detectors give you visibility into what the model is doing, useful when commercial tools disagree. 3) Different last-update cadences. You want at least one detector with monthly updates and one that’s more stable. Disagreement handling: any 2-of-3 majority is our action threshold. 1-of-3 we flag for human review. 3-of-3 clear we publish. We track disagreement rates over time as a metric of detector drift.
Confirming the ‘different training data’ principle. We use a similar mix at our editorial team. The biggest mistake people make is using 3 detectors that all train on similar data. They agree with each other not because the content is clearly human/AI but because they share blind spots.
The academic literature supports this approach. Detector ensemble methods consistently outperform single-detector accuracy in benchmarks. The ‘diverse training data’ criterion is essentially ensemble diversity, which is well-established in ML.
@SilentBean64 the disagreement-rate-as-drift-metric is brilliant. tracking that going forward. its a free leading indicator of when to add a new detector to the mix.