Algorithmic Fairness in Geo-intelligence Workflows through Causality

Brian K. Masinde*, Caroline M. Gevaert, Michael H. Nagenborg, Marc van den Homberg, Jaap A. Zevenbergen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

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 languageEnglish
Title of host publicationProceedings of the 3rd European Workshop on Algorithmic Fairness
PublisherCEUR
Publication statusPublished - 2024
Event3rd European Workshop on Algorithmic Fairness, EWAF 2024 - Johannes Gutenberg University Mainz, Mainz, Germany
Duration: 1 Jul 20243 Jul 2024
Conference number: 3
https://2024.ewaf.org/home

Publication series

NameCEUR workshop proceedings
PublisherCEUR
Volume3908
ISSN (Print)1613-0073

Conference

Conference3rd European Workshop on Algorithmic Fairness, EWAF 2024
Abbreviated titleEWAF 2024
Country/TerritoryGermany
CityMainz
Period1/07/243/07/24
Internet address

Keywords

  • algorithmic fairness
  • Biases
  • disaster early warning systems
  • geo-intelligence

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