Person Perception
How machines form impressions of individual people, and what a century of accuracy research already established about judging persons.
- VIDEO
Calibration as Co-Equal Criterion
A spoken walkthrough of why confidence–accuracy coupling belongs beside accuracy as a headline criterion, never composited into a single number.
- VIDEO
A Century of Knowing Persons
A spoken walkthrough of the hundred-year research tradition behind person perception, and what that history tells us about measuring whether machines know people accurately.
- VIDEO
The AI as Candidate Instrument
A spoken walkthrough of what it means to treat an AI system that judges people as a candidate psychometric instrument, and the validity battery it has to survive.
- VIDEO
Dose–Response Person-Perception in Machines
A spoken walkthrough of acquaintance effects in machine person-perception, with the token of interaction history as the unit of exposure.
- ARTICLE
Mimicry Is Not Understanding: Persona Simulation Versus Person-Perception in Language Models
The AI industry sells two capabilities under one word: sounding like you understand someone, and actually perceiving them accurately. This article separates persona simulation from person-perception — and explains why only measurement can keep them apart.
- ARTICLE
A Century of Knowing Persons: What Accuracy Research Already Solved — and AI Evaluation Ignores
Machine learning is rediscovering questions personality psychology solved long ago. This article maps four established research traditions onto the machine-judge case and derives testable hypotheses about where AI should be accurate, biased, or simply wrong.
- ARTICLE
The AI as Candidate Instrument: A Full Validity Battery for Machine Judgments of Persons
If an AI system judges the people it talks to, it is a candidate instrument. This article specifies the five-component validity battery — convergence, discrimination, dose–response, calibration, and profile accuracy — needed to test one.
- ARTICLE
Dose–Response Person-Perception: Acquaintance Effects in Machines, With the Token as the Unit of Exposure
How much of your history does a machine need before it knows you? This article specifies a within-subject dose–response design, with the token as the unit of exposure, to draw the curve for machine person-perception.
- ARTICLE
Calibration as Co-Equal Criterion: Confidence–Accuracy Coupling in Machine Judgments of Humans
A system that is confidently wrong about people is a different kind of object than one that is uncertainly wrong. This article argues calibration belongs beside accuracy as a headline criterion, never composited into one number.
- VIDEO
The Unmeasured Instrument
A spoken walkthrough of the argument: AI person-perception is a psychometric problem, and no benchmark yet validates whether machine judgments of people are accurate.
- ARTICLE
Scrubbing the Answer Key: Contamination Control and Pre-Registration Discipline in Person-Perception Evaluation
What it takes to keep a person-perception benchmark honest: four classes of contamination, auditable removal rules, and the pre-registration architecture that keeps findings confirmatory.
- ARTICLE
The Fluent Interaction Conjecture: B = γ(A × L)
A falsifiable conjecture for when knowing a person pays off: benefit is multiplicative in person-model accuracy and communicative load, gated by context.
- ARTICLE
Where Fluency Pays: Communicative Load as the Moderator of Personalization Benefit
A pre-registered prediction and the decisive experiment: accuracy about a person converts into benefit only in proportion to a task’s communicative load.
- ARTICLE
The Halo in the Machine: Discriminant-Validity Failure and Impression Compression in LLM Person-Judgments
Why model assessments of different people look more alike than the people do — and a measurement framework for halo, compression, and discriminant-validity failure.
- ARTICLE
Unearned Intimacy: Anthropomorphism, Trust Miscalibration, and Systems That Claim to Know You
Fluent, remembering, speaking systems generate trust through mechanisms independent of their accuracy about you — a structural, measurable risk.
- ARTICLE
Benchmarks Discipline Industries: Governance Design for a Person-Perception Standard
Four structural commitments that make a benchmark credible when its convener has a commercial stake in the results.
- ARTICLE
Beyond the Stranger: Machine Person-Perception Versus Human Judges Across Acquaintance
A research agenda for the question everyone asks — does the machine know you better than your colleagues, friends, or spouse — asked properly.
- ARTICLE
The Unmeasured Instrument: AI Person-Perception as Psychometrics' Missing Validation Problem
AI systems now judge personality at scale, yet no benchmark validates whether those judgments are accurate. This piece argues that person-perception is a psychometric problem — and sketches what an independent validation standard would require.