A review of explainable AI in the satellite data, deep machine learning, and human poverty domain
Hall, Ola LU ; Ohlsson, Mattias LU
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.
(Less)
- author
- Hall, Ola
LU
; Ohlsson, Mattias
LU
and Rögnvaldsson, Thorsteinn
- organization
- publishing date
- 2022-10
- 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
-
- pmid:36277818
- scopus:85140913941
- 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
- 2025-04-19 03:36:17
@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}},
}