Sigmoidal Calibrator™
Precision Score Calibration for AI Detection
Sigmoidal Calibrator™ converts raw multi-metric aggregates into a calibrated, interpretable AI probability score using a sigmoidal (S-curve) transformation. This prevents the "flat middle" problem of linear aggregation where most texts score near 50%, and ensures scores of 90%+ are reliably AI-generated and scores below 15% are reliably human-written.
How It Works
Raw Score Aggregation
Collects weighted scores from all 12 metrics of Multi-Metric AI Scanner™. Raw aggregate is a float in [0,1] representing the weighted average AI signal strength.
Sigmoidal Mapping
Applies a learned sigmoidal function σ(x) = 1/(1+e^(-k(x-x₀))) where k controls steepness and x₀ is the decision boundary. Parameters are fit on a 50k human+AI corpus to maximize AUC.
Confidence Bands
Computes 95% confidence intervals around the calibrated score based on text length and metric variance. Short texts (< 100 words) receive wider confidence bands.
Score Interpretation
Maps calibrated scores to interpretable bands: < 15% = Human, 15–35% = Likely Human, 35–65% = Uncertain, 65–85% = Likely AI, > 85% = AI-Generated.
Key Metrics
Use Cases
- Converting raw detection scores to human-interpretable probabilities
- Reducing false confidence in borderline AI/human texts
- Calibrated threshold-setting for enterprise content policies
- Research benchmarking with well-calibrated classifier outputs
Try Sigmoidal Calibrator™ Now
Powered by our proprietary technology stack. Available directly in the TextHumanize tools and via REST API.