12 KPIs × 5 Categories
Criteria & Weights
Each KPI carries a different weight depending on the evaluation category. Weights reflect the relative importance of each metric to the category's primary use case. All weights sum to 100% within each category.
Weight Distribution Matrix
| KPI | Text | Visual | Voice | Auth | Academic |
|---|---|---|---|---|---|
| Detection Accuracy | 20% | 20% | 20% | 10% | 20% |
| False Positive Rate | 15% | 15% | 15% | 5% | 20% |
| Model Coverage | 12% | 10% | 10% | 5% | 10% |
| Speed/Throughput | 5% | 5% | 8% | 5% | 3% |
| Transparency | 10% | 8% | 8% | 15% | 8% |
| API Availability | 5% | 5% | 5% | 8% | 3% |
| Privacy Compliance | 8% | 8% | 10% | 15% | 12% |
| Audit Status | 5% | 5% | 5% | 12% | 5% |
| Update Frequency | 8% | 8% | 8% | 5% | 5% |
| Cross-Language | 5% | 5% | 5% | 5% | 8% |
| Accessibility | 3% | 3% | 3% | 5% | 5% |
| Research | 4% | 8% | 3% | 10% | 1% |
Scoring Formula
Raw scores for each KPI are normalized to a 0-100 scale using min-max normalization across the evaluation universe. The overall integrity score is calculated as the weighted sum of normalized KPI scores, using the category-specific weight distribution shown above. Platforms are ranked by descending overall score within each category and across the full index. The resulting top 10 is explained use case by use case in The Best AI Detector of 2026.
Overall Score = Σ (KPIi × Weighti) for i = 1 to 12
where KPIi = (rawi − mini) / (maxi − mini) × 100
where KPIi = (rawi − mini) / (maxi − mini) × 100