Measuring base-rate bias error in workplace safety investigators
Bibliographic record
Abstract
Introduction: The current research explored the magnitude of professional industrial investigators’ bias to attribute cause to a person more readily than to situational factors, i.e., human error bias. Such biased opinions may relieve companies from responsibilities and liability, as well as, compromise efficacy of suggested preventative measures. Method: Professional investigators and undergraduate participants were given a summary of a workplace event and asked to allocated cause to the factors they found causal for the event. The summary was crafted to be objectively balanced in its implication of cause equally between two factors: a worker and a tire. Participants then rated their confidence and the objectivity of their judgment. We then conducted an effect size analysis which supplemented the findings from our experiment with two previously published research studies that used the same event summary. Results: Professionals exhibited a human error bias, but nevertheless believed that they were objective and confident in their conclusions. The lay control group also showed this human error bias. These data, along with previous research data, revealed that given the equivalent investigative circumstances, this bias was significantly larger with the professional investigators, with an effect size of dunb = .97, than the control group with an effect size of only dunb = .32. Conclusions: The direction and strength of the human error bias can be quantified, and is shown to be larger in professional investigators compared to the lay people.Practical Applications: Understanding the strength and direction of bias is a crucial step in mitigating the effects of the bias. The results of the current research demonstrate that mitigation strategies such as proper investigator training, a strong investigation culture and standardized techniques, are potentially promising interventions to mitigate human error bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.124 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".