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 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 the vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligent algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by perdition of vulnerable to future poverty households? In this paper, we utilize a multidimensional and longitudinal welfare data comprising one and a half million individual 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.
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.
Commenter: Hamed Khalili
Commenter's Conflict of Interests: Author
Affiliation added.