Building a Smarter Government Using Machine Learning Applications: Benefits and Challenges

Eirini Manga (), Nikitas Karanikolas () and Catherine Marinagi ()
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Eirini Manga: University of West Attica
Nikitas Karanikolas: University of West Attica
Catherine Marinagi: Agricultural University of Athens

A chapter in Digital Economy and Green Growth, 2024, pp 77-98 from Springer

Abstract: Abstract This paper aims to investigate the benefits and the challenges of the utilization of Machine Learning (ML) applications in public administration. The importance of ML applications to the revolutionization of public administration and the enhancement of the quality of provided services is discussed. As the volume of data available to government organizations continues to grow, ML plays a crucial role in extracting useful insights. ML can be used to analyze big data and identify patterns and trends that may not be easily discernible by human analysts, providing valuable knowledge that can be used to improve decision-making, leading to more informed and effective choices. The potentials of ML applications in the significant improvement of the efficiency and effectiveness of public administration and government operations are also discussed. ML can assist in fraud detection, crisis management, fairness in criminal justice, prediction and protection of traffic accidents, identification of risks from citizen reports, air quality prediction, water quality prediction, healthcare prediction and decision-making, energy management, forecasting crime in urban transportation, resource usage utilization, sentiment analysis, predictive maintenance, and building chatbots to support public services. Additionally, the challenges of the utilization of ML applications into government operations are addressed, including technological, ethical, and organizational issues. Technological issues concern the difficulties that arise from the deployment of ML applications on outdated legacy systems. Ethical issues concern data quality, data privacy and security, responsible and ethical design of ML algorithms, and explainable outcomes of ML algorithms that ensure accuracy, fairness, transparency, and equity. Organizational issues concern the increase of government officials’ awareness and understanding of ML algorithms.

Keywords: Machine learning; e-Government; Public administration; Big data; Chatbots; O33 Technological change: choices and consequences; diffusion processes, M150 IT management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-031-66669-8_4

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DOI: 10.1007/978-3-031-66669-8_4

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