It’s not a bug, it’s a feature: How AI experts and data scientists account for the opacity of algorithms
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 opacity of machine learning (ML) algorithms is a significant concern in academic and regulatory circles. An emergent sociology of algorithms, however, argues that far from opacity being an inherent quality of algorithms, it is socially constructed and contingent upon certain choices and decisions. In this article, we show that a valorization of opacity is a key component of the epistemic culture of ML experts. While earlier campaigns for mechanical objectivity contrasted the inconsistency of human experts with the reliability of procedures and machines, we found that ML experts valorize precisely those moments when complex algorithms 'surprised' them with unexpected outcomes. They thereby endowed machines with a mysterious capacity to make predictions based on calculations and factors that humans cannot grasp. In this way, they turned opacity from a problem into an epistemic virtue. We trace this valorization of opacity to the jurisdictional struggles through which ML expertise emerged and differentiated itself from its two competitors: the 'expert systems' type of the 'artificial intelligence' sub-field of computer science on the one hand and inferential statistics on the other. In the course of these struggles, ML experts absorbed a theory of human expertise as tacit and inarticulable, extended it to include algorithms, and then leveraged this newly acquired version of opacity to dramatize the differences that separated them from statisticians. The analysis is based on sixty in-depth, semi-structured, and open-ended interviews with ML experts and data scientists working today, as well as historical research on the origins of data science.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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