CHAPTER F-5 COVID-19 and Accountable Artificial Intelligence in a Global Context
Bibliographic record
Abstract
This chapter identifies two of the key elements in accountable artificial intelligence infrastructure globally-ethical modelling and responsible data.The chapter takes a global perspective and highlights issues of particular relevance to countries that were already in humanitarian crises, such as food insecurity and conflict, explaining how these play into the way that epidemiological models should be constructed.Furthermore, it examines vulnerability from the perspective of aid recipients and migrants, to evoke the type of guidelines and laws that should be taken into account for data protection and privacy. Résumé La COVID-19 et l'intelligence artificielle responsable dans un contexte mondialCe chapitre aborde deux des principaux éléments d'une infrastructure d'intelligence artificielle responsable à l'échelle mondiale : la * Full Professor of Law at the Faculty Law (Civil Law Section) of the University of Ottawa, and member of the Center of Law, Technology and Society.** Senior data scientist focusing on anticipatory financing of humanitarian crisis, on joint appointment at UNOCHA's Centre for Humanitarian Data and the World Bank's Disaster Risk Financing Unit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".