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Bo Schwartz Madsen

dblp:262/0164 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › interpretability › explainable AI
explainable prediction
0.712023
An Explainable Forecasting System for Humanitarian Needs Assessment · AAAI 2023
Machine learning › Trustworthy machine learning
interpretability
0.712023
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
YearPublicationVenuePosition
2023 An Explainable Forecasting System for Humanitarian Needs Assessment
abstract
We 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
AAAI2