Technology on trial: facilitative and prejudicial effects of computer-generated animations on jurors’ legal judgments
Bibliographic record
Abstract
The current study explored how a computer-generated animation (CGA) illustrating a defendant’s version of events affected jurors’ judgments in a mock second-degree murder trial. We hypothesized that mock jurors who viewed a CGA illustrating the defendant’s testimony would be more likely to acquit compared to those who viewed static visual images or did not view a visual aid, and that this effect would occur regardless of whether the narrative depicted in the CGA was corroborated by pertinent testimonial evidence. In this 2 (testimony congruence: incongruent vs.congruent) x 3 (testimony modality: no-aid vs.static visual aid vs. computer-generated animation) between-subjects design, undergraduate students (N = 238) read a transcript from a fictitious trial and heard the defendant’s testimony in one of three modalities. Across congruence conditions, participants were significantly more likely to acquit the defendant when his testimony was accompanied by a CGA (OR= 5.08), compared to a static visual aid or with no-aid. Our results suggest that CGAs may have a disproportionate impact on jurors’judgments compared to traditional forms of demonstrative evidence. Whether this impact is facilitative or prejudicial, however, depends on whether the content of the animation is congruent or incongruent with other case evidence.
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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 | 0.004 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".