Anticipatory action and resilience in the Sahel pastoral region: Geoethical considerations in exploring the potential of coupling a regional early warning system with simulation modelling as a semi-qualitative case study in the development sector
Main Article Content
Abstract
Artificial Intelligence (AI) and machine learning have been taking an increasing role in analysing and monitoring socioeconomic vulnerability, especially regarding food systems in relation to geohazards. These methods require a large amount of data that are not always available at the field level, nor are they exempt from bias. Instead, more empirical qualitative approaches, such as case studies, seem more appropriate when analysing human-geosphere intersections. Therefore, efforts need to be made to establish how a case study approach can better inform AI and machine learning, what is the added value, and how do decision makers avoid missing important developments in anticipatory action.
The case study approach may help current AI methods to make them more reliable and better; hence, there is interest in benefitting from them. Case studies are suitable for explaining complexity through data triangulation. At the same time, they allow a quick rate of return in terms of understanding complex interrelations between humans and nature, particularly when related to climate change and conflict risk assessments. Furthermore, they can be used together with machine learning methods to calibrate the validity of results and can, especially, be used as training data in machine learning. Finally, and perhaps most importantly, case studies bring transparency to scientific methods because they are not an extractive method, but apply iterative heuristics recognising the users’ experience and giving legitimacy to results; in turn, it is necessary to ensure impact and durability of decisions in the humanitarian and development sector. However, case studies are labour intensive and, therefore, it is only possible to have a limited number of case studies that serve to inform extrapolation methods using AI and machine learning techniques.
This paper makes a conceptual review of the, as yet, unresolved inter-linkages of risk, vulnerability, resilience, and adaptation concepts, suggesting a georisks adaptive governance framework considering geoethical principles. Furthermore, it provides an example of how to apply this framework by coupling an early warning system in the Sahel region with systems dynamic modelling under a case study approach in order to observe the impact of adaptation strategies in relation to cultural resilience in food systems in the development and humanitarian sector.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.