Special Track 5

Special Track 5

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Title: AI in public decision-making processes

Track Chairs:

Sarah Giest, Leiden University (corresponding author)

Bram Klievink, Leiden University

Alex Ingrams, Leiden University

Author keywords:

artificial intelligence

public decision-making processes

implementation of algorithms

human-machine interaction

governance of AI


In this track, we cover public decision-making processes with artificial intelligence. In other words, the application of ‘systems that display intelligent behaviour by analyzing their environment and taking actions – with some degree of autonomy – to achieve specific goals’ in the public sector (EC 2018). Public administrations are increasingly utilizing AI in governmental decisions and this track specifically addresses the effects of that on bureaucrats, citizens and system designers. The human-machine interaction in these decision-making processes is of particular relevance given concerns around potential bias inherent to automated or partially-automated decisions in combination with how this affects public service delivery for citizens as well as discretionary space of bureaucrats (e.g., Barocas and Selbst 2016; Giest and Samuels 2020; Kuziemskia and Misuraca 2020).

Utilizing AI in public decision-making processes leads to changes in how daily work is performed and cases are handled for bureaucrats. This is especially striking for street-level bureaucrats (Lipsky 1980), since their defining characteristic is the physical interaction between bureaucrats and citizen-clients. Automated decision-making procedures have the goal of standardizing procedures and essentially limiting the role of idiosyncrasies and reducing complexity. They however also restrict the discretion of bureaucrats. Citizens are at the receiving end of automated decisions on, for example, benefit calculations. Some scholars see a new digital divide emerging where algorithms are used to make decisions about social services that amplify and reproduce social inequality (Gran et al. 2020). Finally, system designers are often decoupled from these processes and require input by citizens and bureaucrats in the form of contextual knowledge and lived experiences. There is evidence that a disconnect between those using the system and those creating it already occurs in the design stage of the system (Volg et al. 2020).

In this context, we invite theoretical and empirical papers that address the following dimensions:

  • AI-supported decision-making at street-level where services are provided to citizens;
  • Decision-making around the technical structure and the design of the algorithm;
  • Decision-making on the deployment and implementation of algorithms in organisations and by professionals;
  • Consequences of AI-supported decision-making for citizens.

With this, we aim to contribute to an emerging field around the use of AI in government that covers topics on accountability, transparency and explainability (e.g., Lepri et al., 2018; Criado et al. 2020; Dencik and Kaun 2020).


Barocas, S., and A.D. Selbst. 2016. Big data’s disparate impact. California Law Review, 104(3), 671-732.

Criado, J.I., Valero, J., and J. Villodre. 2020. Algorithmic transparency and bureaucratic discretion: The case of SALER early warning system. Information Policy 25(4), 449-470.

Dencik, L., and A. Kaun. 2020. Datafication and the welfare state. Global Perspectives 1(1).

European Commission (EC). 2018. Coordinated Plan on Artificial Intelligence. Brussels, COM(2018) 795 final. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52018DC0795&rid=3.

Giest, S., and A. Samuels. 2020. ‘For good measure’: data gaps in a big data world. Policy Sciences 53, 559-569.

Gran, A.-B., Booth, P. and T. Bucher. 2020. To be or not to be algorithm aware: a question of a new digital divide? Information, Communication & Society, DOI: 10.1080/1369118X.2020.1736124.

Kuziemski, M., and Misuraca, G. 2020. AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy 44(6), 101976.

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., and P. Vinck. 2018. Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology, 31(4), 611-627.

Lipsky, M. 1980. Street-Level Bureaucracy: Dilemmas of the Individual in Public Services. New York: Russell Sage Foundation.

Vogl, T.M., Seidelin, C., Ganesh, B. and J. Bright. 2020. Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence in UK Local Authorities. Public Administration Review, DOI: 10.1111/puar.13286.


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