Demo Data from EDBT 2026 / reviewers in the wild / expert
Bo Schwartz Madsen
dblp:262/0164
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-9848-6525ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › interpretability › explainable AI
explainable prediction |
0.7 | 1 | 2023 | An Explainable Forecasting System for Humanitarian Needs Assessment · AAAI 2023 |
Machine learning › Trustworthy machine learning
interpretability |
0.7 | 1 | 2023 | An Explainable Forecasting System for Humanitarian Needs Assessment · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
scenario analysis · 1.3machine learning · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | An Explainable Forecasting System for Humanitarian Needs AssessmentabstractWe present a machine learning system for forecasting forced displacement populations deployed at the Danish Refugee Council (DRC). The system, named Foresight, supports long term forecasts aimed at humanitarian response planning. It is explainable, providing evidence and context supporting the forecast. Additionally, it supports scenarios, whereby analysts are able to generate forecasts under alternative conditions. The system has been in deployment since early 2020 and powers several downstream business functions within DRC. It is central to our annual Global Displacement Report which informs our response planning. We describe the system, key outcomes, lessons learnt, along with technical limitations and challenges in deploying machine learning systems in the humanitarian sector. Rahul Nair 0004, Bo Schwartz Madsen, Alexander Kjærum |
AAAI | 2 |