Cognitive bias in workplace investigation: Problems, perspectives and proposed solutions
Bibliographic record
Abstract
Psychological research demonstrates how our perceptions and cognitions are affected by context, motivation, expectation, and experience. A mounting body of research has revealed the many sources of bias that affect the judgments of experts as they execute their work. Professionals in such fields as forensic science, intelligence analysis, criminal investigation, medical and judicial decision-making find themselves at an inflection point where past professional practices are being questioned and new approaches developed. Workplace investigation is a professional domain that is in many ways analogous to the aforementioned decision-making environments. Yet, workplace investigation is also unique, as the sources, magnitude, and direction of bias are specific to workplace environments. The workplace investigation literature does not comprehensively address the many ways that the workings of honest investigators’ minds may be biased when collecting evidence and/or rendering judgments; nor does the literature offer a set of strategies to address such happenings. The current paper is the first to offer a comprehensive overview of the important issue of cognitive bias in workplace investigation. In it I discuss the abilities and limitations of human cognition, provide a framework of sources of bias, as well as, offer suggestions for bias mitigation in the investigation process.
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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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".