Candor's accuracy metrics, methodology, and academic foundations — presented transparently for the analysts, investigators, and integrity teams who need more than marketing claims.
When Candor flags deception, it has never been wrong on our 100-sample validation dataset. Every positive deception flag corresponded to a confirmed deceptive sample. Precision: 100%.
| Metric | Value | At Threshold | Interpretation |
|---|---|---|---|
| Precision | 100% | 42 | Zero false positives |
| Recall | 64% | 42 | 32 of 50 deceptive caught |
| F1 Score | 0.78 | 42 | Balanced precision/recall |
| Accuracy | ~76% | 42 | 76 of 100 correct |
| Human Baseline | ~54% | — | DePaulo et al., 2003 |
| Dataset Size | 100 samples | — | 50 deceptive + 50 truthful |
Research consistently shows humans detect deception at near-chance levels. Candor operates well above that floor.
Meta-analysis of 206 studies involving 24,483 judges (DePaulo et al., 2003). Trained professionals — judges, police, interrogators — perform only marginally better than untrained individuals.
On matched 100-sample validation set. Critically, zero false positives — investigators are never chasing a false lead when Candor flags text as deceptive.
Note: Candor's recall of 64% means 36% of deceptive samples are missed at threshold 42. Lowering the threshold increases recall at the cost of some precision. Teams can tune this for their risk tolerance via the API.
Our validation used a balanced, real-world dataset sourced from published academic corpora — not synthetic text, not crowdsourced opinion.
100 samples total — 50 deceptive, 50 truthful — drawn from peer-reviewed academic corpora across four distinct communication domains.
All samples sourced from published academic corpora with established ground truth labels. No crowdsourced judgments, no synthetic text generation. Real-world documents with independently verified deceptive/truthful status.
Single threshold evaluation at score 42. Candor assigns a 0–100 deception score; samples scoring ≥ 42 are classified as deceptive. Threshold selected to maximize F1 while maintaining perfect precision.
Candor analyzes cognitive load markers, linguistic distancing, lexical density, hedging patterns, and statement coherence — grounded in Criteria-Based Content Analysis (CBCA) and Reality Monitoring (RM) frameworks.
Candor is built on peer-reviewed research spanning two decades — from foundational linguistic psychology to cutting-edge NLP. The founder is a published researcher in this field.
Candor's founder is a published researcher in linguistic deception detection. The 2025 paper "An ERP exploration of the perception of text-based deception" discovered the vN400 — a novel neural marker showing the brain distinguishes lies from truths in text within ~400ms. Conducted at Utah State University with ERP methodology, this work provides the neuroscience foundation for why text-based deception detection works at all.
Candor's scores are probabilistic assessments based on linguistic patterns identified in academic research. They are not legal determinations of deception, fraud, or guilt. A high deception score indicates linguistic features associated with deceptive communication — it does not constitute proof that an individual lied.
Validation is ongoing. Our current dataset of 100 samples provides a meaningful initial benchmark but is not exhaustive. Performance may vary across languages, cultural contexts, text lengths, and communication domains not represented in the validation set. Candor is a decision-support tool — professional judgment remains essential.
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