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AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings

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  • Kuziemski, Maciej
  • Misuraca, Gianluca

Abstract

The rush to understand new socio-economic contexts created by the wide adoption of AI is justified by its far-ranging consequences, spanning almost every walk of life. Yet, the public sector's predicament is a tragic double bind: its obligations to protect citizens from potential algorithmic harms are at odds with the temptation to increase its own efficiency - or in other words - to govern algorithms, while governing by algorithms. Whether such dual role is even possible, has been a matter of debate, the challenge stemming from algorithms' intrinsic properties, that make them distinct from other digital solutions, long embraced by the governments, create externalities that rule-based programming lacks. As the pressures to deploy automated decision making systems in the public sector become prevalent, this paper aims to examine how the use of AI in the public sector in relation to existing data governance regimes and national regulatory practices can be intensifying existing power asymmetries. To this end, investigating the legal and policy instruments associated with the use of AI for strenghtening the immigration process control system in Canada; “optimising” the employment services” in Poland, and personalising the digital service experience in Finland, the paper advocates for the need of a common framework to evaluate the potential impact of the use of AI in the public sector. In this regard, it discusses the specific effects of automated decision support systems on public services and the growing expectations for governments to play a more prevalent role in the digital society and to ensure that the potential of technology is harnessed, while negative effects are controlled and possibly avoided. This is of particular importance in light of the current COVID-19 emergency crisis where AI and the underpinning regulatory framework of data ecosystems, have become crucial policy issues as more and more innovations are based on large scale data collections from digital devices, and the real-time accessibility of information and services, contact and relationships between institutions and citizens could strengthen – or undermine - trust in governance systems and democracy.

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  • Kuziemski, Maciej & Misuraca, Gianluca, 2020. "AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, Elsevier, vol. 44(6).
    • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300689
    DOI: 10.1016/j.telpol.2020.101976
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    References listed on IDEAS

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    1. Arundel, Anthony & Bloch, Carter & Ferguson, Barry, 2019. "Advancing innovation in the public sector: Aligning innovation measurement with policy goals," Research Policy, Elsevier, vol. 48(3), pages 789-798.
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    3. Veale, Michael & Van Kleek, Max & Binns, Reuben, 2018. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making," SocArXiv 8kvf4, Center for Open Science.
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    13. Sergey A. Revyakin, 2022. "Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow," Public Administration & Development, Blackwell Publishing, vol. 42(5), pages 281-292, December.
    14. Sini V. Pillai & Ranjith S. Kumar, 2021. "The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(4), pages 375-389, December.
    15. Rocco, Salvatore, 2022. "Implementing and managing Algorithmic Decision-Making in the public sector," SocArXiv ex93w, Center for Open Science.
    16. Alkinoos Psarras & Theodoros Anagnostopoulos & Ioannis Salmon & Yannis Psaromiligkos & Lazaros Vryzidis, 2022. "A Change Management Approach with the Support of the Balanced Scorecard and the Utilization of Artificial Neural Networks," Administrative Sciences, MDPI, vol. 12(2), pages 1-15, May.
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