methodology & validity
What these numbers are — and what they are not.
This tool maps facial-analysis output to established psychometric models. Read this before acting on any card.
How scores are computed
The normalized profile feeds established models — Big Five, HEXACO, Dark Triad, and EQ-i — via weighted averages of research-derived coefficients. Safety cards are computed on top of those models and the normalized metrics.
Safety, stability & predictability cards
Each card cites its evidence and shows a data-coverage indicator. Effect sizes below describe trait→outcome links measured by questionnaires — not face→outcome links.
Aggression & CWB
Berry, Ones & Sackett (2007)
Agreeableness ↔ interpersonal deviance r≈−.46; conscientiousness ↔ organizational deviance r≈−.42.
Integrity
Ones, Viswesvaran & Schmidt (1993)
Integrity ↔ job performance ρ≈.34, ↔ CWB ρ≈.32–.47; HEXACO H ↔ CWB r≈−.42.
Self-Control
Gottfredson & Hirschi (1990); Pratt & Cullen (2000)
Low self-control ↔ crime and deviance r≈.26–.28.
Manipulation (Dark Triad)
O'Boyle et al. (2012)
Mach/narcissism/psychopathy ↔ CWB r≈.23/.35/.32.
Reliability
Schmidt & Hunter (1998)
Conscientiousness ↔ job performance ρ≈.31 — strongest non-cognitive predictor.
Stress Tolerance
EQ-i / trait-EI meta-analyses
Stress management ↔ job performance ρ≈.25.
Scientific limits of inference from a face
Treat every output as a probabilistic impression from an image, not a measurement or diagnosis.
Boundaries of use — EU AI Act & GDPR
Under the EU AI Act, biometric categorisation and emotion recognition are restricted, and in hiring or education contexts may be prohibited or classed as high-risk. Facial data is special-category personal data under the GDPR.
This tool must not be used for:
- Clinical diagnosis
- Judicial or criminological conclusions
- Hiring, employment, or promotion decisions
- Individual "threat" assessment
It is intended as behavioural, claim-bounded insight for non-consequential, exploratory use.