TY - GEN
T1 - Data-driven decision support systems in e-governance
T2 - leveraging AI for policymaking
AU - Arora, Anudeep
AU - Vats, Prashant
AU - Tomer, Neha
AU - Kaur, Ranjeeta
AU - Saini, Ashok Kumar
AU - Shekhawat, Sayar Singh
AU - Roopak, Monika
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
PY - 2024/1/3
Y1 - 2024/1/3
N2 - Data-driven decision support systems have been used more and more in e-governance as a result of the digital revolution. In order to improve the efficacy and efficiency of policymaking, this research article investigates the integration of artificial intelligence (AI) approaches into e-governance systems. Governments can access enormous volumes of data, and AI algorithms are used to analyze and extract insightful data that enables decision-making based on facts. The article emphasizes the advantages of using AI in the e-governance space while formulating policy. Decision support systems can analyze and understand complicated information by utilizing cutting-edge machine learning and data analytics approaches, revealing trends, patterns, and correlations that would be challenging for human analysts to manually find. As a result, decision-makers in government may make well-informed choices based on impartial research and data. The paper also examines the difficulties and factors to be considered when implementing AI in decision support systems for e-governance. With an emphasis on the significance of responsible AI governance frameworks, ethical issues, including algorithmic bias, transparency, and accountability are addressed. The article also explores the effects of incorporating AI into decision-making processes, including potential sociopolitical effects and the requirement for stakeholder participation and public confidence. The results of this study show how data-driven decision support systems may revolutionize e-governance policies when equipped with AI technology. Governments may enhance their decision-making processes and the outcomes of governance by using the power of big data and sophisticated analytics. This will lead to better public service delivery.
AB - Data-driven decision support systems have been used more and more in e-governance as a result of the digital revolution. In order to improve the efficacy and efficiency of policymaking, this research article investigates the integration of artificial intelligence (AI) approaches into e-governance systems. Governments can access enormous volumes of data, and AI algorithms are used to analyze and extract insightful data that enables decision-making based on facts. The article emphasizes the advantages of using AI in the e-governance space while formulating policy. Decision support systems can analyze and understand complicated information by utilizing cutting-edge machine learning and data analytics approaches, revealing trends, patterns, and correlations that would be challenging for human analysts to manually find. As a result, decision-makers in government may make well-informed choices based on impartial research and data. The paper also examines the difficulties and factors to be considered when implementing AI in decision support systems for e-governance. With an emphasis on the significance of responsible AI governance frameworks, ethical issues, including algorithmic bias, transparency, and accountability are addressed. The article also explores the effects of incorporating AI into decision-making processes, including potential sociopolitical effects and the requirement for stakeholder participation and public confidence. The results of this study show how data-driven decision support systems may revolutionize e-governance policies when equipped with AI technology. Governments may enhance their decision-making processes and the outcomes of governance by using the power of big data and sophisticated analytics. This will lead to better public service delivery.
KW - Artificial intelligence
KW - Blockchain
KW - Data security
KW - Decision support system
KW - E-governance
KW - Policymaking
UR - https://www.scopus.com/pages/publications/85181982293
U2 - 10.1007/978-981-99-8479-4_17
DO - 10.1007/978-981-99-8479-4_17
M3 - Conference contribution
SN - 9789819984794
SN - 9789819984787
T3 - Lecture Notes in Networks and Systems
SP - 229
EP - 243
BT - Artificial Intelligence
A2 - Sharma, Harish
A2 - Chakravorty, Antorweep
A2 - Hussain, Shahid
A2 - Kumari, Rajani
PB - Springer Nature
ER -