Reproducibility of Implicit Association Test (IAT) – Case Study of Meta-Analysis of Racial Bias Research Claims
Bibliographic record
Abstract
The Implicit Association Test, IAT, is widely used to measure hidden (subconscious) human biases – implicit bias – of many topics of interest: race, gender, age, ethnicity, religion stereotypes. There is a need to understand the reliability of these measures as they are being used in many decisions in society today. A study was undertaken to independently test the reliability of (ability to reproduce) racial bias research claims of Black−White relations based on IAT (implicit bias) and explicit bias measurements using statistical p-value plots. These claims were for IAT−real-world behavior correlations and explicit bias−real-world behavior correlations of Black−White relations in a meta-analysis. The p-value plots were constructed using data sets from the meta-analysis and the plots exhibited considerable randomness for all correlations examined. This randomness supports a lack of correlation between IAT (implicit bias) and explicit bias measurements with real-world behaviors of Whites towards Blacks. These findings were observed for microbehaviors (measures of nonverbal and subtle verbal behavior) and person perception judgments (explicit judgments about others). Findings of the p-value plots were consistent with the meta-analysis research claim that the IAT provides little insight into who will discriminate against whom. It was also observed that the amount of real-world variance explained by the IAT and explicit bias measurements was small – less than 5%.
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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 | 0.345 | 0.649 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.038 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".