Debiasing the Outcome Effect: The Role of Instructions in an Audit Litigation Setting
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
The outcome effect occurs where an evaluator, who has knowledge of the outcome of a judge's decision, assesses the quality of the judgment of that decision maker. If the evaluator has knowledge of a negative outcome, then that knowledge negatively influences his or her assessment of the ex ante judgment. For instance, jurors in a lawsuit brought against an auditor for alleged negligence are informed of an undetected fraud, even though an unqualified opinion was issued. This paper reports the results of an experiment in an applied audit judgment setting that examined methods of mitigating the outcome effect by means of instructions. The results showed that simply instructing or warning the evaluator about the potential biasing effects of outcome information was only weakly effective. However, instructions that stressed either (1) the cognitive nonnormativeness of the outcome effect or (2) the seriousness and gravity of the evaluation ameliorated the effect significantly. From a theoretical perspective, the results suggest that there may both motivational and cognitive components to the outcome effect. In all, the findings suggest awareness of the outcome effect and use of relatively nonintrusive instructions to evaluators may effectively counteract the potential for the outcome bias.
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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 | 0.008 | 0.065 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it