AI Detector Comparison & Consensus Tool

Compare AI detection scores from GPTZero, Turnitin, Originality.ai, and Copyleaks. Calculate ensemble consensus averages, dispersion spread, and adjusted risk.

Detector Probability Scores

Sample Scenarios
Adjusted Consensus Risk
45.0%
Moderate / Mixed Signals
0% Human100% Synthetic

Mean Score

43.8%

Spread (Δ)

13.0%

Min Score

38.0%

Max Score

51.0%

Detector Breakdown Matrix
GPTZero45.0%
Turnitin AI38.0%
Originality.ai51.0%
Copyleaks41.0%

Zero Telemetry Privacy

Scores and analytical comparisons are evaluated entirely inside local browser memory.

Ensemble Consensus Mathematics & Variance Adjustment Equations

Combining outputs from independent AI classifiers reduces single-model variance and provides a balanced statistical consensus:

1. Arithmetic Ensemble Mean & Spread Equations
Ensemble Mean μ =
∑_{i=1}^N S_iN
,   Spread Δ = max(S_i) - min(S_i)
2. Uncertainty-Adjusted Risk Index Formula
R_{adj} = min(100, μ + 0.10 × Δ)
Step-by-Step Consensus & Risk Computation Breakdown
Step 1: Input Validation & Boundary Clamping
Filter valid scores in the range [0%, 100%].
Step 2: Dispersion Range & Variance Scaling
Calculate dispersion spread Δ = (Max - Min) and apply 10% penalty weighting.
Step 3: Adjusted Risk Index & Categorization
Ensemble Risk=45.0% (Moderate / Mixed Signals)

AI Detector Benchmark Thresholds & Interpretation

Consensus RangeRisk ClassificationRecommended Action
0% – 34%Likely Human-WrittenReady for submission / publication
35% – 69%Mixed / Disputed SignalsManually revise repetitive sentences & passive voice
70% – 100%High AI ProbabilitySubstantial rewriting required to meet human baseline

Frequently Asked Questions

Why should I compare multiple AI detection platforms?
Different AI detectors (such as GPTZero, Turnitin, Originality.ai, and Copyleaks) use distinct classifier architectures, perplexity metrics, and burstiness thresholds. Cross-referencing scores provides a reliable ensemble signal rather than relying on a single fallible detector.
What does the 'Score Spread (Δ)' metric indicate?
Spread is the difference between the highest and lowest reported scores across all detectors. A narrow spread (e.g. <15%) indicates strong consensus, while a wide spread (>35%) signifies high classification ambiguity.
How is the Adjusted Consensus Risk calculated?
The engine calculates the arithmetic mean of all valid inputs and applies a variance penalty: `Adjusted Risk = Mean + 0.10 × Spread`. This accounts for model divergence and penalizes outlier uncertainty.
Can AI detectors produce false positives on human writing?
Yes. Non-native English writers, highly structured technical writing, and standardized academic papers often trigger false positives due to uniform sentence lengths and predictable vocabulary.

Related Tools