Bibliographic record
Abstract
In this article, we examine the concerning trend of increasing physiognomic artificial intelligence (AI) applications, such as those assessing an individual’s employability, criminality, and even sexual orientation, and attempt to understand why trust may be given to these systems despite their biased and baseless conclusions. While the practice of linking facial features to human characteristics should be rightfully rejected at its core due to the harmful prejudices and antithesis it poses to responsible AI development, this still has not stopped the recent influx of literature claiming to provide unbiased insights in this field of study. To understand this trend, we examine these claims through the lens of a trust model to hypothesize why such applications are gaining acceptance. After reviewing recent literature for common trends, it appears that these applications are gaining acceptance and trust under the guise of big data through the use of exceptionally large datasets, cognitive bias toward believing the output of mathematical frameworks, and data dredging to find relationships justifying their physiognomic hypothesis. As such, we show that when these factors are combined, each contributing toward various dispositions to trust, it unfortunately leads to situations where acceptance of their faulty results becomes a plausible reality, harming individuals affected by their outputs.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".