Artificial Intelligence for Sustainable Humanitarian Logistics
Bibliographic record
Abstract
Artificial intelligence (AI) can improve operational processes by utilizing faster computational capabilities, data, and innovative algorithms. This article reviews the latest research on the applications of AI technology to sustainable humanitarian logistics (SHL) through the sustainability lens. In a broad sense, the cultural, economic, environmental, and societal pillars of the quadruple bottom line (QBL) are covered. Examples of AI-based logistics and supply chain tools already in use in non-profit, humanitarian organizations are emphasized. The authors then conclude that AI can assist SHL in its goal of saving as many lives as possible during disasters while embracing the QBL pillars. As for all emerging technologies, smoothening the collaboration between humans and AI during operations requires a fundamental change in mindset and culture. Moreover, all stakeholders involved in SHL (e.g., public, government, help organizations, the environment, cultures) are affected. There is therefore room for future research on why, when, where, and how to better utilize AI within SHL contexts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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; both teacher heads agree on what is shown here.
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".