Version 1
: Received: 1 September 2023 / Approved: 11 September 2023 / Online: 11 September 2023 (13:49:28 CEST)
Version 2
: Received: 14 November 2023 / Approved: 27 November 2023 / Online: 27 November 2023 (09:49:48 CET)
Khalili, H. (2023). A Bayesian Approach to Examine the Feasibility of Integrating Machine Learning to Recognize Households’ Eligibility in a Basic Income System.
Khalili, H. (2023). A Bayesian Approach to Examine the Feasibility of Integrating Machine Learning to Recognize Households’ Eligibility in a Basic Income System.
Khalili, H. (2023). A Bayesian Approach to Examine the Feasibility of Integrating Machine Learning to Recognize Households’ Eligibility in a Basic Income System.
Khalili, H. (2023). A Bayesian Approach to Examine the Feasibility of Integrating Machine Learning to Recognize Households’ Eligibility in a Basic Income System.
Abstract
Appeals to governments for implementing basic income systems are contemporary. The theoretical background of the basic income notion, only prescribes transferring equal amounts to individuals irrespective of their socioeconomic attributes. However, the most recent basic income initiatives all around the world are attached to certain attributes of the households to become the eligible receivers. Iran is known as the first country in the world to provide a de facto based on the definition basic income to all its citizens irrespective of their socioeconomic attributes. Since the recent years and in the face of budget constraints, the Iranian government has been attempting to consider a set of rules with regard to the welfare attributes of the receiver households to become eligible. This approach is facing significant challenges with regard to appropriate classification of the relative vulnerable from the relative wealthier groups. Can integrating machine learning contribute to reliable recognition of households’ eligibility? In this paper, we analyze this question by utilizing the official welfare statistics of one and a half million Iranian citizens and a Bayesian network approach. Our analysis provides insight into whether machine learning will forward the future of the original basic income notion towards an intelligible direction.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
