Automation of Government Services

Automating government processes involves data collection and digitization via information and communication technologies (ICTs), artificial intelligence (AI), and sometimes also machine learning (ML) (Paul, Jolley & Anthony, 2018, p.6). Some common examples of ML in international development include: strengthening early warning systems; situational awareness;

supplementing development data; point-of-service diagnostics; market segmentation; and customer and citizen service interfaces (Paul, et al., 2018). Impacts Automation can improve the efficiency, quality and coverage of service delivery – e.g. automation of a medicine inventory management system in Pakistan led to control over theft, more transparency, better monitoring and evaluation, and more efficient service delivery in a pilot initiative (DFID, 2018).

Automation of aspects of public sector staff recruitment, performance review, management, and monitoring can address nepotistic practices, can lead to efficiency savings on the salary bill, can improve staff and institutional performance, and can increase transparency and trust in institutions, among other impacts. E.g. China’s staff performance reform in the State Administration of Taxation is credited with enabling them
to make the switch from sales tax to value-added tax in record time, and winning the broad support of tax agency staff. It also contributed to increases in the government’s tax revenues; reforms of state and local tax collection; implementation of preferential tax policies; increased participation in international tax cooperation; and a clearer understanding of institutional and individual responsibilities (World Bank, 2018).

ML can generate more precise recommendations, identify new important data factors, and can quantify the relative importance of different factors (Paul, et al., 2018).

ML algorithms can be used to make predictions, and can help identify emerging problems more quickly than traditional, human methods of analysis as they tend to combine a wider number of data sources than human methods (Paul, et al., 2018).