‘Data dregs’ and its implications for AI ethics: Revelations from the pandemic
Bibliographic record
Abstract
Technology giants today preside over vast troves of user data that are heavily mined for profit. The concentration of such valuable data in private hands to serve mainly commercial interests must be questioned. In this article, we argue that if data is the new oil, Big Tech companies possess extensive, encompassing and granular data that is tantamount to premium oil. In contrast, governments, universities and think tanks undertake data collection efforts that are comparatively modest in scale, scope, duration and resolution and must contend with 'data dregs'. Viewed against the backdrop of the COVID-19 pandemic, this sharp data asymmetry is unfortunate because the data Big Tech monopolizes is invaluable for boosting epidemiological control, formulating government policies, enhancing social services, improving urban planning and refining public education. We explain why this state of extreme data inequity undermines societal benefit and subverts our quest for ethical AI. We also propose how it should be addressed through data sharing and Open Data initiatives.
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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 | 0.088 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.125 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.017 | 0.035 |
| 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".