Separating error from bias: A new framework for facial age estimation in humans and AIs
Bibliographic record
Abstract
Abstract Apparent facial age plays an important role in social interactions, serving a meaningful marker of biological aging. Although both humans and AIs achieve reasonable accuracy in estimating age from a person’s face, performance remains imprecise, leaving substantial room for errors and biases. Drawing on principles from classical psychophysics, we demonstrate that the existing literature on age estimation suffers from a critical theoretical and methodological shortcoming, which casts doubt on established findings. We show that the conventional measure used to benchmark the accuracy of human and AI performance is fundamentally confounded by response bias. Consequently, we introduce a novel measure that eliminates this confound. A revised framework based on simulated data, reanalysis of existing data, and new experimental results, reveals fresh insights into how facial age is processed by humans and AIs. Our structure opens up new directions for future research and applications in the study of aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".