Who Do People Prefer to Be in Charge? An In-Depth Analysis of UK Citizens’ Preferences for Politicians,Citizens,Experts,and/or Artificial Intelligence in Policymaking
Restricted accessResearch articleFirst published online February, 2026
Who Do People Prefer to Be in Charge? An In-Depth Analysis of UK Citizens’ Preferences for Politicians,Citizens,Experts,and/or Artificial Intelligence in Policymaking
For a long time, research on democratic governance has primarily focused on preferences for singular decision-makers: elected politicians, citizens, or experts. Recently, however, there has been a shift toward considering preferences for multiple decision-makers, but the specifics of these “multi-actor” models remain unclear. This study addresses this gap by exploring how people prefer decisional authority to be balanced among elected politicians, citizens, (human) experts, and artificial intelligence-based technologies. Analyzing data from over 1500 UK citizens, we identified eight distinct clusters. Four of these clusters (30.8% of the sample) prefer one actor to be dominant in policy decisions (“dominant-actor” clusters), while the other four clusters (69.2% of the sample) prefer co-decision by two or more actors (“multiple-actor” clusters). Overall, the present findings highlight the complexity of decision-making preferences in democratic governance, revealing a strong tendency among citizens toward more “hybrid” decision-making models.
AlexiadouDGunaydinH (2019) Commitment or Expertise? Technocratic Appointments as Political Responses to Economic Crises. European Journal of Political Research58: 845–865.
2.
BauerJ (2022) A Primer to Latent Profile and Latent Class Analysis. In: GollerMKyndtEPaloniemiS, et al. (eds) Methods for Researching Professional Learning and Development: Challenges, Applications and Empirical Illustrations. Cham: Springer, pp.243–268.
3.
Bengtsson ChristensenÅH (2016) Ideals and Actions: Do Citizens’ Patterns of Political Participation Correspond to Their Conceptions of Democracy?Government and Opposition51: 234–260.
4.
BertsouE (2022) Bring in the Experts? Citizen Preferences for Independent Experts in Political Decision-Making Processes. European Journal of Political Research61: 255–267.
5.
BertsouECaramaniD (2022) People Haven’t Had Enough of Experts: Technocratic Attitudes among Citizens in Nine European Democracies. American Journal of Political Science66: 5–23.
6.
BertsouEPastorellaG (2017.) Technocratic Attitudes: A Citizens’ Perspective of Expert Decision-Making. West European Politics40: 430–458.
7.
BovensMZouridisS (2002) From Street-Level to System-Level Bureaucracies: How Information and Communication Technology Is Transforming Administrative Discretion and Constitutional Control. Public Administration Review62: 174–184.
8.
BowlerSDonovanTKarpJA (2007) Enraged or Engaged? Preferences for Direct Citizen Participation in Affluent Democracies. Political Research Quarterly60: 351–362.
9.
BruqueSMoyanoJEisenbergJ (2008) Individual Adaptation to IT-Induced Change: The Role of Social Networks. Journal of Management Information Systems25: 177–206.
10.
BundiPPattynV (2023) Trust, but Verify? Understanding Citizen Attitudes toward Evidence-Informed Policy Making. Public Administration101: 1227–1246.
11.
BusuiocM (2021) Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review81: 825–836.
12.
CarminesEGStimsonJA (1980) The Two Faces of Issue Voting. American Political Science Review74: 78–91.
13.
ClaudyMCAquinoKGrasoM (2022) Artificial Intelligence Can’t Be Charmed: The Effects of Impartiality on Laypeople’s Algorithmic Preferences. Frontiers in Psychology13: 898027.
DaltonRJ (2004) Democratic Challenges, Democratic Choices: The Erosion of Political Support in Advanced Industrial Democracies. Oxford: Oxford University Press.
16.
DaltonRJBurklinWPDrummondA (2001) Public Opinion and Direct Democracy. Journal of Democracy12: 141–153.
17.
De SousaWGDe MeloERPBermejoPHDS, et al. (2019) How and Where Is Artificial Intelligence in the Public Sector Going? A Literature Review and Research Agenda. Government Information Quarterly36: 101392.
18.
ElstubSEscobarO (2019) Defining and Typologising Democratic Innovations. In: ElstubSEscobarO (eds) Handbook of Democratic Innovation and Governance. Cheltenham: Edward Elgar Publishing, pp.11–31.
FariasMNewheiserAKKahaneG, et al. (2013) Scientific Faith: Belief in Science Increases in the Face of Stress and Existential Anxiety. Journal of Experimental Social Psychology49: 1210–1213.
21.
Fernández-MartínezJLFont FábregasJ (2018) The Devil Is in the Detail: What Do Citizens Mean When They Support Stealth or Participatory Democracy?Politics38: 458–479.
22.
FlindersMWoodMCorbettJ (2019) Anti-politics and Democratic Innovation. In: ElstubSEscobarO (eds) Handbook of Democratic Innovation and Governance. Cheltenham: Edward Elgar Publishing, pp.148–160.
23.
FontJWojcieszakMNavarroCJ (2015.) Participation, Representation and Expertise: Citizen Preferences for Political Decision-Making Processes. Political Studies63: 153–172.
24.
GherghinaSGeisselB (2017) Linking Democratic Preferences and Political Participation: Evidence from Germany. Political Studies65: 24–42.
25.
GherghinaSGeisselB (2019) An Alternative to Representation: Explaining Preferences for Citizens as Political Decision-Makers. Political Studies Review17: 224–238.
26.
GoldbergSLindellMBächtigerA (2024) Empowered Minipublics for Democratic Renewal? Evidence from Three Conjoint Experiments in the United States, Ireland, and Finland. American Political Science Review2024: 1–18.
27.
GoldbergSWyssDBächtigerA (2020) Deliberating or Thinking (Twice) About Democratic Preferences: What German Citizens Want from Democracy. Political Studies68: 311–331.
28.
GrönlundKSetäläM (2007) Political Trust, Satisfaction and Voter Turnout. Comparative European Politics5: 400–422.
29.
HaesevoetsTDe CremerDDierckxK, et al. (2021) Human-Machine Collaboration in Managerial Decision Making. Computers in Human Behavior119: 1–11.
30.
HaesevoetsTRoetsAVan SeverenR, et al. (2023) The Public’s Preferred Level of Involvement in Local Policy-Making. Scientific Reports13: 7146.
31.
HaesevoetsTVerschuereBRoetsA (2024a) The Public’s Preferred Balance between Politicians, Citizens, and Experts in Policy-Making Decisions. Policy Studies2024: 1–26.
32.
HaesevoetsTVerschuereBVan SeverenR, et al. (2024b) How Do Citizens Perceive the Use of Artificial Intelligence in Public Sector Decisions?Government Information Quarterly41: 101906.
33.
HartiganJAWongMA (1979) Algorithm AS 136 A KMeans Clustering Algorithm. Applied Statistics28: 100–108.
34.
HelmsL (2023) Why Do Parties Select Non-partisan Ministers? The Paradox of Ministerial Selection in Austria. Representation59: 513–530.
35.
HendriksF (2023) Rethinking Democratic Innovation. Cultural Clashes and the Reform of Democracy. Oxford: Oxford University Press.
36.
HibbingJTheiss-MorseE (2002) Stealth Democracy. Americans’ Beliefs about How Government Should Work. Cambridge: Cambridge University Press.
37.
HibbingJRTheiss-MorseEHibbingMV, et al. (2023) Who Do the People Want to Govern?Party Politics29: 3–15.
38.
KalteneggerMEnnser-JedenastikL (2022) Who’s Fit for the Job? Allocating Ministerial Portfolios to Outsiders and Experts. European Political Science Review14: 618–634.
39.
KaradimasNVPapatzelouKLoumosVG (2007) Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization. In: Artificial Intelligence and Innovations: From Theory to Applications 2007. Boston, MA: Springer.
40.
KaufmanLRousseeuwPJ (1990) Finding Groups in Data. New York: John Wiley & Sons.
41.
KelionL (2019) Crime Prediction Software “Adopted by 14 UK Police Forces.”BBC, 4February. Available at: https://www.bbc.com/news/technology-47118229 (accessed 15 December 2024).
42.
KetchenDJShookCL (1996) The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique. Strategic Management Journal17: 441–458.
43.
KhasawnehOY (2018) Technophobia: Examining Its Hidden Factors and Defining It. Technology in Society54: 93–100.
44.
KlijnEHSkelcherC (2007) Democracy and Governance Networks: Compatible or Not?Public Administration85: 587–608.
45.
KolkmanD (2020) The Usefulness of Algorithmic Models in Policy Making. Government Information Quarterly37: 101488.
46.
KostelkaFSinghSPBlaisA (2022) Is Compulsory Voting a Solution to Low and Declining Turnout? Cross-National Evidence since 1945. Political Science Research and Methods2022: 1–18.
LawsonRGJursPC (1990) New Index for Clustering Tendency and Its Application to Chemical Problems. Journal of Chemical Information and Computer Sciences30: 36–41.
49.
LeeMK (2018) Understanding Perception of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management. Big Data & Society5: 2053951718756684.
50.
MacQueenJ (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probabilities.
51.
Martinho-TruswellE (2018) How AI Could Help the Public Sector. Cambridge, MA: Harvard Business Review.
52.
MisuracaGVan NoordtCBoukliA (2020) The Use of AI in Public Services: Results from a Preliminary Mapping across the EU. In: Proceedings of the 13th international conference on theory and practice of electronic governance (ICEGOV 2020), Athens, 23–25 September, pp. 90–99. New York: ACM.
53.
McKnightHCarterMClayP (2009) Trust in Technology: Development of a Set of Constructs and Measures. DIGIT 2009 Proceedings.
54.
NagtegaalR (2021) The Impact of Using Algorithms for Managerial Decisions on Public Employees’ Procedural Justice. Government Information Quarterly38: 1–10.
Office for National Statistics (2021) Census Data. Available at: https://www.ons.gov.uk/census (accessed 11 December 2024).
59.
OuattaraEvan der MeerT (2023) Distrusting Democrats: A Panel Study into the Effects of Structurally Low and Declining Political Trust on Citizens’ Support for Democratic Reform. European Journal of Political Research62: 1101–1121.
60.
PanelSPaulisEPiletJB, et al. (2024) The Lure of Technocrats: A Conjoint Experiment on Preferences for Technocratic Ministers in Six European Countries. Political Behavior46: 1961–1984.
61.
PattynSVan HielADhontK, et al. (2012) Stripping the Political Cynic: A Psychological Exploration of the Concept of Political Cynicism. European Journal of Personality26: 566–579.
PharrSJPutnamRD (2000) Disaffected Democracies: What’s Troubling the Trilateral Countries?Princeton, NJ: Princeton University Press.
64.
PiletJBTalukderDSanhuezaMJ, et al. (2020) Do Citizens Perceive Elected Politicians, Experts and Citizens as Alternative or Complementary Policy-Makers? A Study of Belgian Citizens. Frontiers in Political Science2: 567297.
65.
PiletJ-BVittoriDRojonS, et al. (2023) Who Do Europeans Want to Govern? Exploring the Multiple Dimensions of Citizens’ Preferences for Political Actors in Nine European Countries. Party Politics2023: 13540688231153932.
66.
RojonSPiletJBVittoriD, et al. (2023) Which Political Outsiders Do Europeans Prefer as Ministers?European Political Science Review15: 444–464.
67.
RooduijnMVan KesselSFroioC, et al. (2019) The Populist: An Overview of Populist, Far Right, Far Left, and Eurosceptic Parties in Europe.
68.
RubioDLastraC (2019) European Tech Insights 2019: Mapping European Attitudes to Technological Change and Its Governance. Center for the Governance of Change. Available at: https://www.ie.edu/cgc/research/tech-opinion-poll-2019/ (accessed 11 December 2024).
69.
ŠaradínPLebedaTLysekJ, et al. (2021) Post-election Survey Data: Local Democracy and the 2018 Local Elections in the Czech Republic. Data in Brief36: 107039.
70.
SchwaigerVBächtigerA (2024) ‘Stick to the Status Quo’? A Conjoint Experiment with German Adolescents on Democratic Designs. European Journal of Political Research. Epub ahead of print 23 May. DOI: 10.1111/1475-6765.12697.
71.
SørensenETorfingJ (2005) The Democratic Anchorage of Governance Networks. Scandinavian Political Studies28: 195–218.
72.
TibshiraniRWaltherGHastieT (2001) Estimating the Number of Data Clusters Via the Gap Statistic. Journal of the Royal Statistical Society B63: 411–423.
Van der MeerTWG (2017) Political Trust and the “Crisis of Democracy.” In: ThompsonWR (ed.) Oxford Research Encyclopedia of Politics. Oxford: Oxford University Press, pp. 1–22.
76.
Van HielAVan AsscheJHaesevoetsT, et al. (2022) A Radical Vision of Radicalism: Political Cynicism, Not Incrementally Stronger Partisan Positions, Explains Political Radicalization. Political Psychology43: 3–28.
77.
Van NoordtCMisuracaG (2022) Artificial Intelligence for the Public Sector: Results of Landscaping the Use of AI in Government across the European Union. Government Information Quarterly39: 101714.
78.
VittoriDRojonSPiletJB (2024) Beyond Parliamentarism: How Do Citizens Want to Decide on Divisive Policies?Comparative European Politics2024: 1–31.
79.
WaterschootJ (2020) It Is Not the Quantity of Motivation That Matters“: A Statistical Comparison between K-means Cluster Analysis and Latent Profile Analysis (Unpublished). Ghent: Ghent University.
80.
WescheJSSondereggerA (2019) When Computers Take the Lead: The Automation of Leadership. Computers in Human Behavior101: 197–209.
81.
WojcieszakM (2014) Preferences for Political Decision-Making Processes and Issue Publics. Public Opinion Quarterly78: 917–939.
82.
ZuiderwijkAChenYCSalemF (2021) Implications of the Use of Artificial Intelligence in Public Governance: A Systematic Literature Review and a Research Agenda. Government Information Quarterly38: 10157.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
