Facial Trustworthiness Predicts Extreme Criminal-Sentencing Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Untrustworthy faces incur negative judgments across numerous domains. Existing work in this area has focused on situations in which the target's trustworthiness is relevant to the judgment (e.g., criminal verdicts and economic games). Yet in the present studies, we found that people also overgeneralized trustworthiness in criminal-sentencing decisions when trustworthiness should not be judicially relevant, and they did so even for the most extreme sentencing decision: condemning someone to death. In Study 1, we found that perceptions of untrustworthiness predicted death sentences (vs. life sentences) for convicted murderers in Florida (N = 742). Moreover, in Study 2, we found that the link between trustworthiness and the death sentence occurred even when participants viewed innocent people who had been exonerated after originally being sentenced to death. These results highlight the power of facial appearance to prejudice perceivers and affect life outcomes even to the point of execution, which suggests an alarming bias in the criminal-justice system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it