Research output per year
Research output per year
Brian K. Masinde*, Caroline M. Gevaert, Michael H. Nagenborg, Marc van den Homberg, Jaap A. Zevenbergen
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
In this paper, we investigate how causality (causal inference) can be used to detect bias and ensure fairness in geo-intelligence workflows. We investigate the usefulness of such a causality-based approach in the context of an early warning system that predicts building damage at municipality levels in The Philippines. We use directed acyclic graphs to reason about the causal relationships in the model case study and quantify the relationships using structural equation modelling. Mediation analysis is also used to validate the causal relationships between variables. We find cases of confounder bias and Simpsons paradox that could potentially bias the damage predictions. However we note that the objective and outcome variable in the early warning system needs to be defined in a manner that allows for more nuanced investigation on fairness (i.e., from damage assessment to impact assessment).
Original language | English |
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Title of host publication | Proceedings of the 3rd European Workshop on Algorithmic Fairness |
Publisher | CEUR |
Publication status | Published - 2024 |
Event | 3rd European Workshop on Algorithmic Fairness, EWAF 2024 - Johannes Gutenberg University Mainz, Mainz, Germany Duration: 1 Jul 2024 → 3 Jul 2024 Conference number: 3 https://2024.ewaf.org/home |
Name | CEUR workshop proceedings |
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Publisher | CEUR |
Volume | 3908 |
ISSN (Print) | 1613-0073 |
Conference | 3rd European Workshop on Algorithmic Fairness, EWAF 2024 |
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Abbreviated title | EWAF 2024 |
Country/Territory | Germany |
City | Mainz |
Period | 1/07/24 → 3/07/24 |
Internet address |
Research output: Contribution to conference › Poster › Academic