Technology on Trial: Facilitative and Prejudicial Effects of Computer-Generated Animations on Jurors' Legal Judgments
Bibliographic record
Abstract
The recent emergence of electronic courtrooms (i.e., courtrooms that are equipped with advanced digital technologies) has generated novel ways to present evidence to jurors. Computer-generated animations, which recreate or illustrate the alleged sequence of events in a crime, are increasingly being used by lawyers to present testimonial evidence to jurors. The current study used a 3 (modality: oral vs. static visual vs. animation) x 2 (congruence: incongruent vs. congruent) between-subjects design to investigate whether presentation modality and evidence congruence affect jurors’ ability to properly evaluate evidence and render ‘accurate’ verdicts. In a laboratory setting, mock jurors (N = 238) read a transcript from a fictitious second-degree murder trial. Participants read testimony from eight witnesses, and heard the oral testimony of the defendant with a static visual aid, a computer-generated animation, or no visual aid. Results demonstrated that mock jurors were more likely to acquit the defendant when his testimony was illustrated with a computer-generated animation compared to a static visual aid or with no additional aid. Research in this area can inform the development of evidentiary regulations which adequately govern the admissibility of computer-generated animations in the courtroom, so as ensure that they are used in a way that maintains a defendant’s right to a fair trial. Keywords: computer-generated evidence, computer animations, legal decision-making, information processing, electronic courtrooms
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".