Expectations of how machines use individuating information and base-rates
Bibliographic record
Abstract
Abstract Machines are increasingly used to make decisions. We investigated people’s beliefs about how they do so. In six experiments, participants (total N = 2664) predicted how computer and human judges would decide legal cases on the basis of limited evidence — either individuating information from witness testimony or base-rate information. In Experiments 1 to 4, participants predicted that computer judges would be more likely than human ones to reach a guilty verdict, regardless of which kind of evidence was available. Besides asking about punishment, Experiment 5 also included conditions where the judge had to decide whether to reward suspected helpful behavior. Participants again predicted that computer judges would be more likely than human judges to decide based on the available evidence, but also predicted that computer judges would be relatively more punitive than human ones. Also, whereas participants predicted the human judge would give more weight to individuating than base-rate evidence, they expected the computer judge to be insensitive to the distinction between these kinds of evidence. Finally, Experiment 6 replicated the finding that people expect greater sensitivity to the distinction between individuating and base-rate information from humans than computers, but found that the use of cartoon images, as in the first four studies, prevented this effect. Overall, the findings suggest people expect machines to differ from humans in how they weigh different kinds of information when deciding.
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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 | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".