COVID-19 may be the largest global social and economic disruption since at least the Great Depression but the fact that it came right into the data science “tsunami” and “digital revolution” makes a lot of difference this time. What was before observed to be a gradual and generational shift to a more connected and online lifestyle has within a matter of weeks become everyday practice for almost all age groups and across all demographic segments of our societies. Clearly a lot of credit in this development goes to the perfect storm of technologies made available in the past few decades. Coupled with the concerns around climate challenge and sustainability, it is now a clear necessity—as well as holding huge prospects—to radically rethink how we work, study, shop, socialize, trade, regulate and govern.

July, 17 2020   |   Zeynep Engin & Philip Treleaven

In a recent study [1] we provide a vision of how the data science technologies of artificial intelligence (AI), internet of things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and governance. Clearly, the impact of the “smartification” of public services and national infrastructure will be much more significant than in any other sector, given government’s function and importance to every institution and individual. We present a simple taxonomy of government services to provide an overview of data science automations being deployed by governments worldwide and identify immediate potentials for huge efficiency gains as follows:

  • Public services – supporting better provision of public services during interaction with and delivery to citizens (e.g. online services);
  • Supporting civil servants and service delivery – providing “intelligent” assistance for case management, real-time evidence support for policy decisions, impact and performance monitoring;
  • Public records – improving the infrastructure to maintain public records and correspondence (e.g., personal/citizen data sharing, forms and submissions, back office operations);
  • Physical infrastructure – improving the maintenance and operation of the national physical infrastructure (e.g., smart cities, infrastructure and planning processes, transport and communication, environment monitoring);
  • Laws and statutes – maintaining laws and statutes, managing courts, judiciary, police, etc. (e.g., codification of laws, smart contracts, trials and prosecution, online dispute resolution);
  • Public policy development – providing tools for real-time monitoring of public opinion, new potential for ex ante evaluation (futures, foresight based on rich and diverse data) and ex post evaluation (bringing together large data sets to better evaluate impact, etc.), interactive and dynamic policy formation, enhanced consultations and impact modeling.

Along with the public sector opportunities we review, we also present a range of challenges that come from the very same technologies. A leading cause of public concern is clearly the ownership and stewardship of data, especially given the recent major data breaches, including the alleged case of Cambridge Analytica harvesting 87 million Facebook profiles to manipulate voter behavior in the US. Given that even further sensitivity is involved in the public sector, the technology solutions for trusted data sharing and linkage is, therefore, a core issue. Blockchain and distributed ledger technologies emerge as providing natural solutions for secure data sharing. The technology offers many potential applications to manage all types of contracts, transactions and records efficiently and in a verifiable and permanent way. However, the technology is still in its infancy, especially with regard to security and privacy. The lack of standards, scalability, storage, access, change management and security against cybercriminals can be mentioned as some of the key areas of concern. There are also unintended consequences in particular regarding the environmental and sustainability impacts of the technology, such as the fact that one bitcoin transaction alone uses as much electricity as the average American household in a week, and excessive carbon emissions, especially when coal-based power is used to generate the computer calculations.

For the public sector, the promise of the IoT is automated high-quality data collection and distributed processing through connected sensors and remotely controlled objects. However, this also raises a number of security and privacy concerns both in virtual and physical terms. Within the smart cities framework, for example, the trade-off might be between the advantages of monitoring a city’s infrastructure for energy efficiency, real-time management of traffic flows or increased neighborhood safety, and potential attacks against critical infrastructure, such as power-supply networks, is a crucial question.

In the case of artificial intelligence technologies, a main argument against their use in the public sector is their dependence on data that is often biased, incomplete and/or imperfect; and the lack of transparency of the processes that produce the outcome. In her award-winning book Weapons of Math Destruction, Cathy O’Neil shows how data-driven decisions made through AI systems may be harmful for the important life events of individuals, from job applications to loan and insurance decisions and the sentencing of criminals.

Beyond the issues mentioned, the use of new technologies in the public sector also introduces a number of philosophical discussions, such as the changing public perception of privacy, security and surveillance, or the ownership and exploitation of personal data, therefore providing a rich domain for further interdisciplinary discussion. Although the scope of this paper was mainly the technology, we also recognize the major societal challenges in this domain. Within the context of developed nations in particular, identifying a comprehensive public sector infrastructure and retrofitting the new digital technologies into that infrastructure are both major challenges, given the long-established culture and the sizable populations. Also, the fact that interests and working practices of computing and policy domains typically tend to be very different, there is also the need to educate a new generation of civil servants and to re-train the existing workforce in embracing new technologies to ensure efficiency and continuity in the public sector. Furthermore, the issues around the use of private citizen data, fairness of algorithmic decision-making practices, transparency of public operations, accountability for any damage caused by computer-assisted processes and the natural threat of potential job losses are all extremely valid and timely considerations.

The opportunities afforded by new sources of data, analytic techniques and networked infrastructure for public sector applications are still at early stages of exploration. The ambitions are wide-ranging—from providing better decision support for policymakers and personalized citizen services to automated management of city infrastructures and operations, and to new collaboration models between public and private sectors. However, the pace of adoption is—and has to be—slower than in the commercial sector, given that the stakes are much higher in public decision-making tasks and that governments must prioritize ethical values and individual and public rights along with intended efficiency gains. This, however, must also be balanced with the potential opportunity costs as well as the potential risks of being left behind in the global competition. Overall, given the importance of governments to every sector and individual, we believe they should be the major “client” and also the “public champion” of these new data science technologies and innovations. Although the ethical and legal implications pose an even more complex domain for interdisciplinary and cross-sector discussion, one should also note that there is now the historical opportunity to address some of the persistent human errors, inefficiencies and injustices to improve our collective decision-making processes and deliver services to individual citizens with the help of these technologies.


Zeynep Engin is a Senior Research Associate at the Urban Dynamics Laboratory of UCL, focusing on the development of a national technology platform for urban analytics research and its user communities. She has a first degree in Mathematics and a PhD in statistical pattern detection on visual data, followed by over five years of executive experience in the non-profit sector. Zeynep is also a Policy Fellow at the Centre for Science and Policy (CSaP), University of Cambridge. Philip Treleaven is Professor of Computer Science and Director of the Centre for Doctoral Training in Financial Computing and Analytics, University College London.



[1] Zeynep Engin, Philip Treleaven, “Algorithmic government: Automating public services and supporting civil servants in using data science technologies,” The Computer Journal, Volume 62, Issue 3, March 2019, pages 448–460,

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