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A review of explainable AI in the satellite data, deep machine learning, and human poverty domain

Hall, Ola LU ; Ohlsson, Mattias LU orcid and Rögnvaldsson, Thorsteinn (2022) In Patterns 3(10).
Abstract

Recent advances in artificial intelligence and deep machine learning have created a step change in how to measure human development indicators, in particular asset-based poverty. The combination of satellite imagery and deep machine learning now has the capability to estimate some types of poverty at a level close to what is achieved with traditional household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and, consequently, new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We perform an integrative literature... (More)

Recent advances in artificial intelligence and deep machine learning have created a step change in how to measure human development indicators, in particular asset-based poverty. The combination of satellite imagery and deep machine learning now has the capability to estimate some types of poverty at a level close to what is achieved with traditional household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and, consequently, new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We perform an integrative literature review focusing on three core elements relevant in this context—transparency, interpretability, and explainability—and investigate how they relate to the poverty, machine learning, and satellite imagery nexus. Our inclusion criteria for papers are that they cover poverty/wealth prediction, using survey data as the basis for the ground truth poverty/wealth estimates, be applicable to both urban and rural settings, use satellite images as the basis for at least some of the inputs (features), and the method should include deep neural networks. Our review of 32 papers shows that the status of the three core elements of explainable machine learning (transparency, interpretability, and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries. We argue that explainability is essential to support wider dissemination and acceptance of this research in the development community and that explainability means more than just interpretability.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
machine learning, poverty and satellite imagery, XAI
in
Patterns
volume
3
issue
10
article number
100600
publisher
Cell Press
external identifiers
  • scopus:85140913941
  • pmid:36277818
ISSN
2666-3899
DOI
10.1016/j.patter.2022.100600
language
English
LU publication?
yes
id
149d037d-b19d-454b-88d1-78937f65031a
date added to LUP
2022-12-13 14:35:48
date last changed
2024-06-29 01:31:08
@article{149d037d-b19d-454b-88d1-78937f65031a,
  abstract     = {{<p>Recent advances in artificial intelligence and deep machine learning have created a step change in how to measure human development indicators, in particular asset-based poverty. The combination of satellite imagery and deep machine learning now has the capability to estimate some types of poverty at a level close to what is achieved with traditional household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and, consequently, new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We perform an integrative literature review focusing on three core elements relevant in this context—transparency, interpretability, and explainability—and investigate how they relate to the poverty, machine learning, and satellite imagery nexus. Our inclusion criteria for papers are that they cover poverty/wealth prediction, using survey data as the basis for the ground truth poverty/wealth estimates, be applicable to both urban and rural settings, use satellite images as the basis for at least some of the inputs (features), and the method should include deep neural networks. Our review of 32 papers shows that the status of the three core elements of explainable machine learning (transparency, interpretability, and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries. We argue that explainability is essential to support wider dissemination and acceptance of this research in the development community and that explainability means more than just interpretability.</p>}},
  author       = {{Hall, Ola and Ohlsson, Mattias and Rögnvaldsson, Thorsteinn}},
  issn         = {{2666-3899}},
  keywords     = {{machine learning; poverty and satellite imagery; XAI}},
  language     = {{eng}},
  number       = {{10}},
  publisher    = {{Cell Press}},
  series       = {{Patterns}},
  title        = {{A review of explainable AI in the satellite data, deep machine learning, and human poverty domain}},
  url          = {{http://dx.doi.org/10.1016/j.patter.2022.100600}},
  doi          = {{10.1016/j.patter.2022.100600}},
  volume       = {{3}},
  year         = {{2022}},
}