Abstract
Technological innovation has shaped and transformed society again and again over the course of history. In this context, the advent of the “digital age” is no exception. However, what has changed is the speed with which waves of new technologies advance and spread. Such innovations as the Internet and mobile phones have emerged and wrought profound changes on societies across the globe within the space of only a few decades. And there are still more such disruptive technologies on the horizon, that is, Artificial Intelligence (AI) and the Internet of Things (IoT). Consequently, technology-driven new business models, the geographical dispersion of production, and the invention of new products and services are all challenging traditional governance patterns and modes of operation around the world and how governments are perceived by citizens. In this context, this chapter focuses on how disruptive technologies affect the role of the State and on the new challenges they pose to policymakers against the underlying conundrum. On the one hand, there is widespread belief that the judicious use of technology is imperative for economic and social development and a potential key accelerator and enabler for many, perhaps all, of the 17 Sustainable Development Goals (SDGs). On the other hand, however, we still need to fully understand, and adapt to, the governance challenges that arise as a consequence of this unprecedented rate of technological progress. Thus, this chapter looks at how the process of technology adoption interacts with the policy cycle taking into account the three-dimensional relationship between government and technological innovation, since government assumes three different roles, that is, user, regulator, and promoter of innovation. It also summarises briefly what are considered to be some of the most significant technological trends of today and the particular opportunities and challenges they pose for policymakers. In doing this, it singles out some of the key policy issues that emerge from the disruptive nature of such technologies, while at the same time it discusses their implications on public governance. It concludes with a number of key takeaways.
This chapter is based on the background paper of the 2017 International Conference on Disruptive Technologies and the Public Service held in Singapore on 28–29 September 2017. The Conference was hosted jointly by the UNDP Global Centre for Public Service Excellence (GCPSE), the Nanyang Technopreneurship Centre (NTC) at Nanyang Technical University, Singapore, and the Centre for Economics and Public Administration (CEPA).
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Notes
- 1.
- 2.
Structural barriers to that result from the resistance of the relevant technology support networks to adapt to new technologies, as observed by Zeleny (2009), can be found in the public, as well as in the private sector.
- 3.
The observation that “we tend to overestimate the effect of a technology in the short run and underestimate its effect in the long run” (“Amara’s Law”) is commonly attributed to Roy C. Amara, a former long-time president of the Institute for the Future, Menlo Park (CA).
- 4.
An approach frequently termed “frugal innovation”.
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- 6.
Note: 2011 online edition.
- 7.
National reports are submitted to the UN by Member States (https://sustainabledevelopment.un.org/memberstates). The SDG Index and Dashboards Report (http://www.sdgindex.org/) provides a report card for country performance on the Agenda 2030 and the Sustainable Development Goals (SDGs). It is produced by the Sustainable Development Solutions Network (SDSN) and the Bertelsmann Stiftung Foundation.
- 8.
Ibid.
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- 11.
“2017 International Conference on Disruptive Technologies and the Public Service” (28–29 September 2017, Singapore) http://www.undp.org/content/undp/en/home/ourwork/global-policy-centres/publicservice/convening-hub/techPS2017.html
- 12.
- 13.
- 14.
The phrase “cognitive computing”, coined by IBM, is used frequently as a generic term for systems that “learn at scale, reason with purpose and interact with humans naturally” (Kelly 2015).
- 15.
For example, Searle’s “Chinese room” argument (Searle 1980).
- 16.
A botnet is a number of Internet-connected devices, each of which is running one or more bots. Botnets can be used to perform distributed denial-of-service attack (DDoS attack), steal data, [1] send spam, and allows the attacker to access the device and its connection. The owner can control the botnet using command and control (C&C) software. The word “botnet” is a combination of the words “robot” and “network”. The term is usually used with a negative or malicious connotation.
- 17.
For example, the “Microsoft Ireland” case (Microsoft Corp. v. United States, 829 F.3d, pg. 200) regarding the extraterritorial application of a US search warrant to user data held by Microsoft on one of its servers located abroad.
- 18.
For example, the “Tallinn Manual on the International Law Applicable to Cyber Operations” published by the North Atlantic Treaty Organization (NATO) Cooperative Cyber Defence Centre of Excellence (NATO Cooperative Cyber Defence Centre of Excellence 2017b).
- 19.
Commission Decision of 27 June 2017, Case Reference AT.39740 Google Search (Shopping), C (2017) 4444 final of 27 June 2017.
- 20.
- 21.
- 22.
UNDP Report “Social Contract in Times of Conflict and Fragility?” 2016. http://www.undp.org/content/undp/en/home/librarypage/democratic-governance/conflict-prevention/the-social-contract-in-situations-of-conflict-and-fragility.html
- 23.
The Law on Network Searches (Netzwerkdurchsuchungsgesetz) was passed by the German Bundestag on 30 June 2017.
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Leitner, C., Stiefmueller, C.M. (2019). Disruptive Technologies and the Public Sector: The Changing Dynamics of Governance. In: Baimenov, A., Liverakos, P. (eds) Public Service Excellence in the 21st Century. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-13-3215-9_8
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