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The challenges and limits of big data algorithms in technocratic governance
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“and an effective solution to AI-specific risks such as data or algorithmic errors, data or algorithmic bias, or even data-embedded discrimination (Janssen & Kuk, 2016).”
From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Janssen & Kuk, 2016
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
Self-citationØlnes, S., Ubacht, J., & Janssen, M. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.09.007 · Cited by 1,107
“These “algorithms become increasingly autonomous and invisible, they become harder for the public to detect and scrutinize their impartiality status” (Janssen & Kuk, 2016, p. 371).”
BackgroundSelf-citationFairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
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Data governance: Organizing data for trustworthy Artificial Intelligence
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101493 · Cited by 811
“This approach is often advocated as a foundation for data governance (Rothstein, Borraz, & Huber, 2013) and an effective solution to AI-specific risks such as data or algorithmic errors, data or algorithmic bias, or even data-embedded discrimination (Janssen & Kuk, 2016).”
Self-citationWach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review · doi:10.15678/eber.2023.110201 · Cited by 584
“Algorithms can systematically introduce inadvertent bias, reinforce historical discrimination, favour a political orientation or reinforce undesired practices (Janssen & Kuk, 2016).”
BackgroundExploring artificial intelligence adoption in public organizations: a comparative case study
Neumann, O., Guirguis, K., & Steiner, R. (2024) · Public Management Review · doi:10.1080/14719037.2022.2048685 · Cited by 451
“…focuses on adoption factors of AI in the public sector, it does not consider further the application risks within the public sector (Eubanks 2017; Janssen and Kuk 2016; Maciejewski 2017; O’Neil 2016), nor does it discuss the question of how public organizations might deal with algorithmic…”
BackgroundInfluentialAn experimental study of public trust in AI chatbots in the public sector
Aoki, N. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101490 · Cited by 350
van Noordt, C., & Misuraca, G. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101714 · Cited by 356
Criado, J. I., & Gil-Garcia, J. R. (2019) · International Journal of Public Sector Management · doi:10.1108/ijpsm-07-2019-0178 · Cited by 339
Wieringa, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372833 · Cited by 321
“Similarly, Fink [65, p. 1454] argues that algorithms should be made inspectable for a ‘broad range of people’, and Janssen and Kuk [82, p. 372] note that citizens should be able to scrutinize the government’s algorithms.”
BackgroundMethodInfluentialAhn, M. J., & Chen, Y. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101664 · Cited by 403
Big data and AI - A transformational shift for government: So, what next for research?
Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020) · Public Policy and Administration · doi:10.1177/0952076718780537 · Cited by 272
“To that end, using automated algorithms to support human decision-making is suggested to significantly reduce fraud and errors in service processing (Janssen and Kuk, 2016).”
MethodKummitha, R. K. R. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101481 · Cited by 272
“Techno-driven approach does not take 'context' into consideration, rather expects technologies to fix all the glitches in cities (Janssen and Kuk, 2016) .”
Public value creation in digital government
Panagiotopoulos, P., Klievink, B., & Cordella, A. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.101421 · Cited by 265
“Conflicts in public value creation enabled by algorithmic governance could be identified in trade-offs between efficiency, fairness and transparency or other unintended consequences (e.g. Danaher et al., 2017; Janssen & Kuk, 2016).”
BackgroundThe perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making
de Bruijn, H., Warnier, M., & Janssen, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101666 · Cited by 264
Self-citationHow Data Science Workers Work with Data
Muller, M. J., Lange, I., Wang, D., Piorkowski, D., Tsay, J., Liao, Q. V., Dugan, C., & Erickson, T. D. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300356 · Cited by 300
“Janssen and Kuk called for enhanced algorithmic transparency in “unravelling the imperceptibility, material-ity and governmentality of how algorithms work ” (em-phasis in original) [46].”
BackgroundTransparent to whom? No algorithmic accountability without a critical audience
Kemper, J., & Kolkman, D. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2018.1477967 · Cited by 260
“The increased prominence of algorithmic models in organizational decision-making and the speed at which new data science techniques are developed and adopted, have been case for concern and has led many to call for increased transparency (Hildebrandt, 2012; Janssen & Kuk, 2016; Pasquale, 2015).”
BackgroundValle-Cruz, D., Criado, J. I., Sandoval‐Almazán, R., & Ruvalcaba-Gómez, E. A. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101509 · Cited by 322
National strategic artificial intelligence plans: A multi-dimensional analysis
Fatima, S., Desouza, K. C., & Dawson, G. S. (2020) · Economic Analysis and Policy · doi:10.1016/j.eap.2020.07.008 · Cited by 216
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022) · Social Science Computer Review · doi:10.1177/0894439320980118 · Cited by 192
“Algorithms can introduce inadvertent bias, reinforce historical discrimination, favor a particular political orientation, or reinforce undesired practices (Janssen & Kuk, 2016b).”
BackgroundMethodInfluentialSelf-citationOvercoming the Challenges of Collaboratively Adopting Artificial Intelligence in the Public Sector
Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S., & Esteve, M. (2022) · Social Science Computer Review · doi:10.1177/0894439320979953 · Cited by 162
Wang, C., Teo, T. S. H., & Janssen, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102401 · Cited by 161
Self-citationMeijer, A., Lorenz, L., & Wessels, M. (2021) · Public Administration Review · doi:10.1111/puar.13391 · Cited by 174
Understanding artificial intelligence ethics and safetyPreprint
Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 308
The impact of using algorithms for managerial decisions on public employees' procedural justice
Nagtegaal, R. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2020.101536 · Cited by 166
The value of Big Data in government: The case of ‘smart cities’
Löfgren, K., & Webster, C. W. R. (2020) · Big Data & Society · doi:10.1177/2053951720912775 · Cited by 161
“There is a wide-spread belief that Big Data is going to revolutionize processes associated with undertaking research and predicting human behavior (González-Bail on, 2013; Janssen and Kuk, 2016).”
BackgroundInfluentialMoving beyond Smart Cities: Digital Nations for Social Innovation & Sustainability
Kar, A. K., Ilavarasan, P. V., Gupta, M. P., Janssen, M., & Kothari, R. (2019) · Information Systems Frontiers · doi:10.1007/s10796-019-09930-0 · Cited by 141
Self-citationThe role of privacy policy on consumers’ perceived privacy
Chang, Y., Wong, S. F., Libaque-Sáenz, C. F., & Lee, H. (2018) · Government Information Quarterly · doi:10.1016/j.giq.2018.04.002 · Cited by 160
Public engagement and AI: A values analysis of national strategies
Wilson, C. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101652 · Cited by 140
Young, M. M., Bullock, J. B., & Lecy, J. D. (2019) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvz014 · Cited by 255
“…information technologies on government practices, but neither names artificial intelligence as its empirical focus nor considers the unique theoretical implications of these systems for administrative discretion and decision making (Anthopoulos and Reddick 2016; Janssen and Kuk 2016; Meijer 2018).”
BackgroundConsiderations for AI fairness for people with disabilities
Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N., & Manser, E. (2019) · AI Matters · doi:10.1145/3362077.3362086 · Cited by 143
“Owing to intended or unintended bias in the data, the model may inadvertently enforce or reinforce discriminatory patterns that work against people with disabilities (Janssen & Kuk, 2016).”
BackgroundRanerup, A., & Henriksen, H. Z. (2022) · Social Science Computer Review · doi:10.1177/0894439320980434 · Cited by 139
“In order to capture digital discretion in this context and these types of tasks, we will explore the details of the interaction between humans and technology (Janssen & Kuk, 2016; Kitchin, 2017).”
BackgroundMethodInfluentialAlgorhythmic governance: Regulating the ‘heartbeat’ of a city using the Internet of Things
Coletta, C., & Kitchin, R. (2017) · Big Data & Society · doi:10.1177/2053951717742418 · Cited by 136
“Here, governance is delegated to digital infrastructures (DeNardis and Musiani, 2016) and enacted through forms of ‘automated management’ (Dodge and Kitchin, 2007), wherein human and social action are increasingly mediated by black-boxed algorithms that are inaccessible to public scrutiny (Gillespie, 2014; Janssen and…”
BackgroundAlgorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities
Lim, H. S. M., & Taeihagh, A. (2019) · Sustainability · doi:10.3390/su11205791 · Cited by 136
Urban sustainable transportation planning strategies for livable City's quality of life
Wey, W.-M., & Huang, J.-Y. (2018) · Habitat International · doi:10.1016/j.habitatint.2018.10.002 · Cited by 129
Algorithmic domination in the gig economy
Muldoon, J., & Rækstad, P. (2022) · European Journal of Political Theory · doi:10.1177/14748851221082078 · Cited by 127
“Recent discussions of algorithmic injustice have contributed to calls for greater attention to questions of fairness and accountability including issues of procedural fairness and more substantive approaches focused on interventions into decision outcomes and their social impact (Janssen and Kuk, 2016; Pasquale, 2015;…”
BackgroundSustainable AI: An integrated model to guide public sector decision-making
Wilson, C., & van der Velden, M. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.101926 · Cited by 134
A survey on artificial intelligence assurance
Batarseh, F. A., Freeman, L., & Huang, C. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00445-7 · Cited by 132
Social equity in the data era: A systematic literature review of data‐driven public service research
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Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance
Hassani, H., Huang, X., MacFeely, S., & Entezarian, M. R. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5030028 · Cited by 133
Sætra, H. S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101283 · Cited by 111
“If the exact workings of a system is not known, it is difficult to game the system [21].”
InfluentialEnhancing public service delivery efficiency: Exploring the impact of AI
Kulal, A., Rahiman, H. U., Suvarna, H., Abhishek, N., & Dinesh, S. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100329 · Cited by 165
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“Technocratic actors from governments to non-profits believe that “datafication” can inform better decisionmaking, both in real-time and the long term (Janssen and Kuk, 2016; Krasmann, 2020).”
BackgroundInfluentialTechno-optimism and policy-pessimism in the public sector big data debate
Vydra, S., & Klievink, B. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.05.010 · Cited by 118
“…making the (political) choices made along the way difficult to interrogate (as demonstrated by, for example, the COMPAS algorithm) by the very people that (have to) use these statements and insights as a basis for policy (Janssen & Kuk, 2016b; van der Voort, Klievink, Arnaboldi, & Meijer, 2018).”
BackgroundMethodInfluentialResponsible governance of generative AI: conceptualizing GenAI as complex adaptive systems
Janssen, M. (2024) · Policy and Society · doi:10.1093/polsoc/puae040 · Cited by 147
Self-citationOpportunity for renewal or disruptive force? How artificial intelligence alters democratic politics
König, P. D., & Wenzelburger, G. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101489 · Cited by 123
Data ecosystems for protecting European citizens’ digital rights
Calzada, I., & Almirall, E. (2020) · Transforming Government People Process and Policy · doi:10.1108/tg-03-2020-0047 · Cited by 93
Criado, J. I., & Alcarazo, L. O. D. Z. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101688 · Cited by 113
The loopholes of algorithmic public services: an “intelligent” accountability research agenda
Bracci, E. (2023) · Accounting, Auditing & Accountability Journal · doi:10.1108/aaaj-06-2022-5856 · Cited by 89
Democratising Smart Cities? Penta-Helix Multistakeholder Social Innovation Framework
Calzada, I. (2020) · Smart Cities · doi:10.3390/smartcities3040057 · Cited by 86
“Thus, the aggregated use of digital data to monitor, surveil, and nudge citizens has resulted in an intrinsic version of the technocratic smart city [ 13–15], which pervasively bypasses the democratic accountability to which stakeholders are entitled.”
Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector
Chen, Y., Ahn, M. J., & Wang, Y. (2023) · Sustainability · doi:10.3390/su15064796 · Cited by 135
“From this perspective, the technical components of an AI system include data, algorithms, computer systems, and robotics [8,32,49,50], while the social components include people, organizations, governance rules, and interactions.”
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Acting like an algorithm: digital farming platforms and the trajectories they (need not) lock-in
Carolan, M. (2020) · Agriculture and Human Values · doi:10.1007/s10460-020-10032-w · Cited by 86
Travaini, G. V., Pacchioni, F., Bellumore, S., Bosia, M., & Micco, F. D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710594 · Cited by 93
“This systematic review is a thorough synthesis of the best available evidence, but it is also a contribution in a field that presents many ethical, deontological and legal critical issues in the state of the art.”
BackgroundLarsen, A. G., & Følstad, A. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101927 · Cited by 80
Transparency and accountability in digital public services: Learning from the Brazilian cases
Saldanha, D. M. F., Dias, C. N., & Guillaumon, S. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101680 · Cited by 108
Making governance agile: Exploring the role of artificial intelligence in China’s local governance
Li, Y., Fan, Y., & Nie, L. (2023) · Public Policy and Administration · doi:10.1177/09520767231188229 · Cited by 97
“…scholars argue that such inherent challenges of the algorithms can be mitigated through unraveling the imperceptibility, materiality, and governmentality of Li et al. how algorithms work (Janssen and Kuk, 2016b), and thus the algorithmic governance or AI governance becomes more and more important.”
Factors influencing the use of artificial intelligence in government: Evidence from China
Zhang, W., Zuo, N., He, W., Li, S., & Yu, L. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101675 · Cited by 74
Susha, I., Grönlund, Å., & van Tulder, R. J. M. (2018) · Government Information Quarterly · doi:10.1016/j.giq.2018.11.002 · Cited by 69
The usefulness of algorithmic models in policy making
Kolkman, D. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101488 · Cited by 75
A taxonomy of human–machine collaboration: capturing automation and technical autonomy
Simmler, M., & Frischknecht, R. (2020) · AI & Society · doi:10.1007/s00146-020-01004-z · Cited by 81
“There are, for example, taxonomies that classify socio-technical systems according to their degree of automation and complexity (Janssen and Kuk 2016).”
BackgroundKönig, P. D., & Wenzelburger, G. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101688 · Cited by 87
Wang, G., Guo, Y., Zhang, W., Xie, S., & Chen, Q. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101803 · Cited by 75
Opening the government’s black boxes: freedom of information and algorithmic accountability
Fink, K. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2017.1330418 · Cited by 61
“This is particularly important as algorithms are modified and become more complex over time, concentrating power in the hands of people who understand how they work (Janssen & Kuk, 2016).”
BackgroundArtificial Intelligence and Democracy
Duberry, J. (2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781788977319 · Cited by 68
Data in design: How big data and thick data inform design thinking projects
Mortati, M., Magistretti, S., Cautela, C., & Dell’Era, C. (2023) · Technovation · doi:10.1016/j.technovation.2022.102688 · Cited by 69
Tangi, L., Müller, A. P. R., & Janssen, M. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102055 · Cited by 88
Self-citationPalma-Ruiz, J. M., Torres-Toukoumidis, Á., González-Moreno, S. E., & Baca, H. G. V. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e08959 · Cited by 55
“Data analytics is expected to provide valuable insights (Desouza and Jacob, 2017; Fosso Wamba et al., 2015; Janssen and Kuk, 2016; Kyriazis etal.,2020;Palma-RuizandG (cid:1) omez-Martínez,2019).”
BackgroundKim, E. S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101378 · Cited by 58
Wang, X., Wu, Y. C., Ji, X., & Fu, H. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1320277 · Cited by 81
“It highlights the need for algorithmic designers to abide by current laws that provide equal protection against discrimination to citizens and consumers (Janssen and Kuk, 2016).”
BackgroundRyan, M., Antoniou, J., Brooks, L., Jiya, T., Macnish, K., & Stahl, B. C. (2021) · Science and Engineering Ethics · doi:10.1007/s11948-021-00293-x · Cited by 65
Wang, G., Xie, S., & Li, X. (2022) · Public Management Review · doi:10.1080/14719037.2022.2070243 · Cited by 56
“…to public administration through the replacement of the human workforce, the limited role of ethics and professional competence, the reinforcement of biases in policymaking, and the lack of supervision and accessibility (e.g. Barth and Arnold 1999; Boyd and Wilson 2017; Janssen and Kuk 2016).”
BackgroundThe effects of different personal data categories on information privacy concern and disclosure
Chua, H. N., Ooi, J. S., & Herbland, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102453 · Cited by 55
“Given that each piece of data leaves behind electronic trails of customer activities, individuals are concerned about how companies collect and use their private information (Janssen & Kuk, 2016; Morey et al., 2015).”
Accuracy and Fairness for Juvenile Justice Risk Assessments
Berk, R. (2019) · Journal of Empirical Legal Studies · doi:10.1111/jels.12206 · Cited by 50
A Framework and Databases for Measuring Entrepreneurial Ecosystems
Johnson, E., Hemmatian, I., Lanahan, L., & Joshi, A. M. (2021) · Research Policy · doi:10.1016/j.respol.2021.104398 · Cited by 49
Seven HCI Grand Challenges Revisited: Five-Year Progress
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“…algorithms often suffer from the very same biases and errors of human decision-makers, or even amplify existing social prej-udices, as shown in a number of real-world cases (Janssen & Kuk, 2016; Kordzadeh & Ghasemaghaei, 2022; Kovacova et al., 2019; Panch et al., 2019; Raghavan et al., 2020).”
Scherr, S., Haim, M., & Arendt, F. (2018) · New Media & Society · doi:10.1177/1461444818801010 · Cited by 51
Algorithmic transparency and bureaucratic discretion: The case of SALER early warning system
Criado, J. I., Valero, J., & Villodre, J. (2020) · Information Polity · doi:10.3233/ip-200260 · Cited by 47
Public AI canvas for AI-enabled public value: A design science approach
Fatima, S., Desouza, K. C., Buck, C., & Fielt, E. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101722 · Cited by 55
Data in Policing: An Integrative Review
Afzal, M., & Panagiotopoulos, P. (2024) · International Journal of Public Administration · doi:10.1080/01900692.2024.2360586 · Cited by 51
“Subsequently, the unstructured, complex, dynamic, and heterogeneous nature of large data sets led to the development of data analytics (Janssen & Kuk, 2016; Mergel et al., 2016; Ruijer et al., 2023).”
Platform-based innovation ecosystems: Entering new markets through holographic strategies
Barile, S., Simone, C., Iandolo, F., & Laudando, A. (2022) · Industrial Marketing Management · doi:10.1016/j.indmarman.2022.07.003 · Cited by 46
Citizen conceptions of democracy and support for artificial intelligence in government and politics
König, P. D. (2022) · European Journal of Political Research · doi:10.1111/1475-6765.12570 · Cited by 52
“Strikingly, the evidence hardly supports the idea that a technocratic notion of democracy is associated with greater AI support – even though theoretical contributions have pointed to a clear affinity between technocracy and a social ordering via AI (e.g., Janssen & Kuk, 2016; Sætra, 2020).”
Canadian smart cities: Are we wiring new citizen‐local government interactions?
Johnson, P. A., Acedo, A., & Robinson, P. J. (2020) · Canadian Geographies / Géographies canadiennes · doi:10.1111/cag.12623 · Cited by 44
Replicating Smart Cities: The City-to-City Learning Programme in the Replicate EC-H2020-SCC Project
Calzada, I. (2020) · Smart Cities · doi:10.2139/ssrn.3689054 · Cited by 40
Public Values in the Age of Big Data: A Public Information Perspective
Ingrams, A. R. (2018) · Policy & Internet · doi:10.1002/poi3.193 · Cited by 48
Whether AI adoption challenges matter for public managers? The case of Polish cities
Sienkiewicz-Małyjurek, K. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101828 · Cited by 46
Mining service quality feedback from social media: A computational analytics method
Lee, H. J., Lee, M., Lee, H., & Cruz, R. A. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101571 · Cited by 46
Kempeneer, S. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101578 · Cited by 48
de Sousa, W. G., Fidelis, R. A., Bermejo, P. H. D. S., Gonçalo, A. G. D. S., & Melo, B. D. S. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101660 · Cited by 42
Transparency in policy making: A complexity view
Brunswicker, S., Priego, L. P., & Almirall, E. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.05.005 · Cited by 41
Forecasting and explaining emergency department visits in a public hospital
Petsis, S., Karamanou, A., Kalampokis, E., & Tarabanis, K. A. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00716-6 · Cited by 35
“…need to be addressed including ethical challenges, such as the lack of trust in AI-based decisions (Sun & Medaglia, 2019) and bias of algorithms in policy making (Janssen & Kuk, 2016), organizational and managerial challenges, such as resistance to data sharing (World Bank, 2020), and others.”
BackgroundGenerating Value from Government Data Using AI: An Exploratory Study
Gao, Y., & Janssen, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-57599-1_24 · Cited by 34
“With the use of AI algorithm, the data collection and processing become less visible [34].”
MethodSelf-citationPolicy capacity and rise of data‐based policy innovation labs
Kim, S., WELLSTEAD, A. M., & Heikkila, T. (2022) · Review of Policy Research · doi:10.1111/ropr.12494 · Cited by 32
Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector
Fischer-Abaigar, U., Kern, C. A., Barda, N., & Kreuter, F. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101976 · Cited by 64
The use of public sector data analytics in the Netherlands
van Veenstra, A. F., Grommé, F., & Djafari, S. (2020) · Transforming Government People Process and Policy · doi:10.1108/tg-09-2019-0095 · Cited by 32
Inteligencia Artificial (y Administración Pública)
Criado, J. I. (2021) · EUNOMÍA. Revista en Cultura de la Legalidad · doi:10.20318/eunomia.2021.6097 · Cited by 38
“…de una característica esencial para entender su potencial: los algoritmos coevolucionan con datos, sistemas y humanos dentro de complejos sistemas sociotécnicos «algorithms dynamically co-evolve with data, systems, and humans within a complex socio-technical system» (Janssen & Kuk, 2016, p. 372).”
BackgroundInfluentialFilgueiras, F. D. B. (2021) · AI & Society · doi:10.1007/s00146-021-01201-4 · Cited by 35
The problem with trust: on the discursive commodification of trust in AI
Krüger, S., & Wilson, C. (2022) · AI & Society · doi:10.1007/s00146-022-01401-6 · Cited by 30
“…2018), processes and policy frameworks that protect citizens in the slightest case of doubt (Balaram et al. 2018; Brundage et al. 2020; Dignum 2019; Janssen and Kuk 2016; Kemper and Kolkman 2019; Kolkman 2020; Lee 2018; Mulgan 2016; Reisman et al. 2018; Vassilakopoulou 2020), or public-sector…”
Background
Metadata
- Authors
- Janssen, M., & Kuk, G.
- Affiliations
- Delft University of Technology (NL)
- Nottingham Trent University (GB)
- Corresponding author
- Janssen, Marijn
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 33
- Issue
- 3
- Pages
- 371-377
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W2527349292
- Cited by
- 363
- References
- 32
- Citations by year
- 2026: 64 · 2025: 42 · 2024: 43 · 2023: 31 · 2022: 48 · 2021: 47 · 2020: 39 · 2019: 27 · 2018: 16 · 2017: 5 · 2016: 1
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Government Information Quarterly (publishedVersion)
- Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- big data algorithms, technocratic governance, algorithmic bias, algorithmic accountability, governmentality, algorithmic transparency, data curation, algorithmic discrimination, institutional logics, algorithmic governance
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