AI and Disaster Risk: A Practitioner Perspective
Bibliographic record
Abstract
Emerging techniques developed by AI researchers promise to offer the capacity to support disaster risk management (DRM), through making data collection or analysis practices faster, less costly, or more accurate. However, in every socially consequential domain in which AI tools have been applied, these technologies have been demonstrated to have some degree of negative consequences. This paper explores an attempt to convene technical experts in the area of DRM to discuss potential negative impacts, their approaches toward mitigating these impacts as well as identifying some of the overarching challenges. In doing so, we contribute new findings about a domain that has received relatively little attention from critical and ethical AI researchers, and the opportunities and limitations that are presented by working with domain experts to evaluate the social consequences of emerging technologies.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".