Authors : Md Kamrul Hasan Chy; Obed Nana Buadi

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/23mp6twn

Scribd : https://tinyurl.com/ywjhumvm

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT687

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper explores how machine learning (ML) can enhance both policy-making and policy evaluation by providing advanced tools for data analysis, predictive modeling, and continuous assessment. ML offers the ability to process vast datasets, uncover patterns, and provide real-time insights, allowing policymakers to make more informed, efficient, and adaptable decisions. By applying ML, governments can predict trends, optimize resource allocation, and tailor interventions to meet the specific needs of various sectors such as healthcare, education, finance, and environmental management. Furthermore, ML supports ongoing policy evaluation by enabling continuous monitoring and adjustment of policies based on up-to-date data. While ML presents transformative potential, challenges related to transparency, bias, and data privacy must be addressed to ensure that its application in policy-making is ethical and fair. This paper highlights the importance of improving ML model explainability and establishing strong legal and regulatory frameworks to maximize its effectiveness in governance.

Keywords : Machine Learning; Policy Making; Policy Evaluation; Security;

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