Race, sex, and emotion affect trust of auditory witness testimony
Bibliographic record
Abstract
Racial and gender stereotypes influence courtroom decisions such as verdict and sentencing, which typically are harmful to Black defendants. How much a witness is trusted swings the balance in courtroom proceedings, and racial and gender biases are also present in this domain. Our voices convey information about gender, trust, emotion, and stereotypes about race. Trust is predicted by by acoustic properties of the voice such as pitch, speech rate, breathiness, and noisiness. To examine potential biases towards witness credibility in light of stereotypes of how Black and white men and women might speak, participants rated whether they trusted audio recordings differing in emotion, sex, and race, speaking “That is exactly what happened”. We found that trust ratings varied significantly between sex and race when voices sounded angry, fearful, and neutral, but not when voices sounded disgusted or sad. Acoustic properties of the voice had different influences on trust ratings when Black and white men and women spoke with different emotions. Uncovering such biases that may occur in a courtroom setting is crucial to ensure all individuals receive equitable treatment in the criminal justice system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".