Empirical Research on Citizen Perceptions of Algorithmic Governance 

Abstract

Algorithmic governance describes a process by which governments use data-driven technologies and algorithmic systems to automate decision-making and deliver public services [5,7]. Examples range from parking enforcement and loan applications to social benefits allocation and criminal sentencing decisions. Rapid uptake of algorithms in public governance raised concerns about opaqueness, privacy risks, biases and lacking accountability of algorithmic systems that may undermine the legitimacy of algorithmic governance [3,11]. Studies show that citizens are also concerned with potential deficiencies of algorithmic systems in the context of public policy [2]. Empirical studies on citizen perceptions is an emerging strand in algorithmic governance research [1,4,6,8,9,10,12,13]. While these studies vary significantly in their design, their findings convey that individual perception of algorithmic governance is nuanced and contextual. At the same time, findings generated in the past 3-5 years demonstrate persistent challenges in studying how citizens conceive of algorithmic governance, what and why concerns them, and how they imagine future governance with algorithms to meet their expectations and live up to the standards of democratic governance. The goal of our panel is to showcase extant empirical research that taps into the citizen perceptions of algorithmic governance. A central theme that connects the papers is trust and perceived trustworthiness of algorithmic systems and actors who develop and deploy them. Papers use different approaches to measuring trustworthiness and legitimacy of algorithmic governance and show high contextual sensitivity, accounting for variation stemming from policy context, political regimes, regulatory models, etc. As a result, our panel will establish state of the art, pinpoint methodological challenges, and outline future research directives in empirical research on citizens’ perspectives on algorithmic governance. 1.Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4). 2.Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 35(3), 611-623. 3.Coglianese, C., & Lehr, D. (2019). Transparency and algorithmic governance. Admin. Law Review, 71, 1.  4.Grimmelikhuijsen, S. (2022). Explaining why the computer says no: algorithmic transparency affects the perceived trustworthiness of automated decision‐making. Public Administration Review. 5.Gritsenko, D., & Wood, M. (2022). Algorithmic governance: A modes of governance approach. Regulation & Governance, 16(1), 45-62.  6.Ingrams, A., Kaufmann, W., & Jacobs, D. (2022). In AI we trust? Citizen perceptions of AI in government decision making. Policy & Internet, 14(2), 390-409. 7.Katzenbach, C., & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4), 1-18.  8.Miller, S., and L. Keiser. 2020. Representative Bureaucracy and Attitudes toward Automated Decision Making. Journal of Public Administration Research and Theory31: 150–65. 9.Starke, C., & Lünich, M. (2020). Artificial intelligence for political decision-making in the European Union: Effects on citizens’ perceptions of input, throughput, and output legitimacy. Data & Policy, 2.  10.Waldman, A., & Martin, K. (2022). Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions. Big Data & Society, 9(1). 11.Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505-523. 

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