Can Data and Machine Learning Change the Future of Basic Income Models? A Bayesian Belief Networks Approach

Hamed Khalili ()
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Hamed Khalili: Research Group E-Government, Faculty of Computer Science, University of Koblenz, D-56070 Koblenz, Germany

Data, 2024, vol. 9, issue 2, 1-18

Abstract: Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligence algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by predicting households vulnerable to future poverty? In this paper, we utilize multidimensional and longitudinal welfare data comprising one and a half million individuals’ data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.

Keywords: basic income; poverty; vulnerability; machine learning; Bayesian networks; artificial intelligence; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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