Element contents
Series: Elements in Public Policy
Artificial Intelligence and Public Policy
Published online by Cambridge University Press: 15 December 2025
Summary
In a global landscape increasingly shaped by technology, artificial intelligence (AI) is emerging as a disruptive force, redefining not only our daily lives but also the very essence of governance. This Element delves deeply into the intricate relationship between AI and the policy process, unraveling how this technology is reshaping the formulation, implementation, and advice of public policies, as well as influencing the structures and actors involved. Policy science was based on practice knowledge that guided the actions of policymakers. However, the rise of AI introduces an unprecedented sociotechnical reengineering, changing the way knowledge is produced and used in government. Artificial intelligence in public policy is not about transferring policy to machines but about a fundamental change in the construction of knowledge, driven by a hybrid intelligence that arises from the interaction between humans and machines.
Keywords
Information
- Type
- Element
- Information
- Series: Elements in Public PolicyOnline ISBN: 9781009572248Publisher: Cambridge University PressPrint publication: 22 January 2026
References
Acemoglu, D. (2024). The simple macroeconomics of AI. Working paper – National Bureau of Economic Research, 32487. https://doi.org/10.3386/w32487.CrossRefGoogle Scholar
Ahn, M. J., Chen, Y. C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664. https://doi.org/10.1016/j.giq.2021.101664.CrossRefGoogle Scholar
Allahyari, M., Pouriyeh, S., Assefi, M., et al. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. Arxiv. https://doi.org/10.48550/arXiv.1707.02919.CrossRefGoogle Scholar
Alon-Barkat, S., Busuioc, M. (2023). Human–AI interactions in public sector decision making: “Automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153–169. https://doi.org/10.1093/jopart/muac007.CrossRefGoogle Scholar
Althaus, M. (2013). Reflections on advisory practice in politics. PSCA—Political Science Applied, 2, 5–15.Google Scholar
Alvarado, R. (2023). AI as an epistemic technology. Science Engineering Ethics, 29, 1–30. https://doi.org/10.1007/s11948-023-00451-3.CrossRefGoogle Scholar
Amershi, S., Cakmak, M., Knox, W. B., Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120. https://doi.org/10.1609/aimag.v35i4.2513.CrossRefGoogle Scholar
Amoore, L. (2024). The deep border. Political Geography, 109, 102547. https://doi.org/10.1016/j.polgeo.2021.102547.CrossRefGoogle Scholar
Amoore, L. (2022). Machine learning political orders. Review of International Studies, 49(1), 20–36. https://doi.org/10.1017/S0260210522000031.CrossRefGoogle Scholar
Amoore, L. (2014). Security and the incalculable. Security Dialogue, 45(5), 423–439. http://dx.doi.org/10.1177/0967010614539719.CrossRefGoogle Scholar
Anthony, C. (2021). When knowledge work and analytical technologies collide: The practices and consequences of black boxing algorithmic technologies. Administrative Science Quarterly, 66(4), 1173–1212. https://doi.org/10.1177/00018392211016755.CrossRefGoogle Scholar
Anthony, J. B. (2020) Managing digital transformation of smart cities through enterprise architecture – a review and research agenda. Enterprise Information Systems, 15(3), 299–331. https://doi.org/10.1080/17517575.2020.1812006.CrossRefGoogle Scholar
Arcas, B. A. (2024). The birth and rebirth of AI, in Lane, M., Sethumadhavan, A. (eds.), Collaborative Intelligence: How Humans and AI are Transforming our World. Cambridge, MA: MIT Press, 21–31.Google Scholar
Bali, A. S., Howlett, M., Lewis, J. M., Ramesh, M. (2021). Procedural policy tools in theory and practice. Policy and Society, 40(3), 295–311. https://doi.org/10.1080/14494035.2021.1965379.CrossRefGoogle Scholar
Banuri, S., Dercon, S., Gauri, V. (2019). Biased policy professionals. The World Bank Economic Review, 33(2), 310–327. https://doi.org/10.1093/wber/lhy033.CrossRefGoogle Scholar
Barandiaran, X., Di Paolo, E., Rohde, M. (2009). Defining agency: Individuality, normativity, asymmetry and spatial temporality in action. Adaptive Behaviour, 17(5), 367–386.CrossRefGoogle Scholar
Bardach, E. (2012). A Practical Guide for Policy Analysis: The Eightfold Path to More Effective Problem Solving. Los Angeles: Sage.Google Scholar
Barzelay, M. (2019). Public Management as a Design-Oriented Professional Discipline. Cheltenham: Edward-Elgar.CrossRefGoogle Scholar
Baumgartner, F. R., Jones, B. D. (2015). The Politics of Information: Problem Definition and the Course of Public Policy in America. Chicago. The University of Chicago Press.Google Scholar
Béland, D. (2019). How Ideas and Institutions Shape Politics and Policy. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Béland, D., Howlett, M. (2016). Instrument constituencies in the policy process. Governance, 29(3), 393–409. https://doi.org/10.1111/gove.12179.CrossRefGoogle Scholar
Bender, E. M., Gebru, T., McMillan-Major, A., Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘21). New York: Association for Computing Machinery, 610–623. https://doi.org/10.1145/3442188.3445922.CrossRefGoogle Scholar
Bengio, Y. (2023). AI and catastrophic risk. Journal of Democracy, 34(4), 111–121.CrossRefGoogle Scholar
Benjamin, R. (2019). Race after Technology: Abolitionist Tools for the New Jim Code. New York: Polity Press.Google Scholar
Blumer, H. (1986). Symbolic Interactionism: Perspective and Method. Berkeley: University of California Press.Google Scholar
Bovens, M., ‘t Hart, P., Kuipers, S. (2008). The politics of policy evaluation, in Goodin, R., Moran, M., Rein., M (eds.), The Oxford Handbook of Public Policy Oxford: Oxford University Press, 319–335.Google Scholar
Braunerhjelm, P., Hepburn, C. (2023). Climate change, complexity, and policy design. Oxford Review of Economic Policy, 39(4), 667–679. https://doi.org/10.1093/oxrep/grad047.CrossRefGoogle Scholar
Buolamwini, J. (2023). Unmasking AI: My Mission to Protect What Is Human in a World of Machines. New York: Random House.Google Scholar
Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S., Esteve, M. (2022). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462–477. https://doi.org/10.1177/0894439320979953.CrossRefGoogle Scholar
Capano, G., Howlett, M. P. (2020). The knowns and unknowns of policy instrument analysis: Policy tools and the current research agenda on policy mixes. SAGE Open, 10(1), 1–13. https://doi.org/10.1177/2158244019900568.CrossRefGoogle Scholar
Capano, G., Malandrino, A. (2022). Mapping the use of knowledge in policymaking: Barriers and facilitators from a subjectivist perspective (1990–2020). Policy Sciences, 55, 399–428. https://doi.org/10.1007/s11077-022-09468-0.CrossRefGoogle Scholar
Chaqués-Bonafont, L., Palau, A. M., Baumgartner, F. (2015). Agenda Dynamics in Spain. London: Palgrave.CrossRefGoogle Scholar
Chatfield, A. T., Reddick, C. G. (2019). A framework for Internet of Things-enabled smart government: A case of IoT cybersecurity policies and use cases in U.S. federal government. Government Information Quarterly, 36(2), 346–357. https://doi.org/10.1016/j.giq.2018.09.007.CrossRefGoogle Scholar
Chen, C., Surette, R., and Shah, M. (2021). Automated monitoring for security camera networks: Promise from computer vision labs. Security Journal, 34, 389–409. https://doi.org/10.1057/s41284-020-00230-w.CrossRefGoogle Scholar
Clifton, J., Pal, L. A. (2022). The policy dilemmas of blockchain, Policy and Society, 41(3), 321–327. https://doi.org/10.1093/polsoc/puac025.CrossRefGoogle Scholar
Coeckelbergh, M. (2021). Narrative responsibility and artificial intelligence: How AI changes human responsibility and sense making. AI & Society, 38(4), 2437–2450. https://doi.org/10.1007/s00146-021-01375-x.CrossRefGoogle Scholar
Coeckelbergh, M. (2023). Democracy, epistemic agency, and AI: Political epistemology in times of artificial intelligence. AI Ethics, 3(1), 1341–1350. https://doi.org/10.1007/s43681-022-00239-4.CrossRefGoogle Scholar
Coeckelbergh, M., Sætra, H. S. (2023). Climate change and the political pathways of AI: The technocracy-democracy dilemma in light of artificial intelligence and human agency. Technology in Society, 75, 102406. https://doi.org/10.1016/j.techsoc.2023.102406.CrossRefGoogle Scholar
Cohen, M. D., March, J. G., Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17(1), 1–25.CrossRefGoogle Scholar
Coles-Kemp, L., Ashenden, D., Morris, A., Yuille, J. (2022). Digital welfare: Designing for more nuanced forms of access. Policy Design and Practice, 3(2), 177–188. https://doi.org/10.1080/25741292.2020.1760414.CrossRefGoogle Scholar
Collington, R. (2021). Disrupting the welfare state? Digitalisation and the retrenchment of public sector capacity. New Political Economy, 27(2), 312–328. https://doi.org/10.1080/13563467.2021.1952559.CrossRefGoogle Scholar
Connolly, R. (2020). Why computing belongs within the social sciences. Communications of the ACM, 63(8), 54–59. https://doi.org/10.1145/3383444.CrossRefGoogle Scholar
Cowls, J., Tsamados, A., Taddeo, M., Floridi, L. (2023). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Society, 38(2), 283–307. https://doi.org/10.1007/s00146-021-01294-x.CrossRefGoogle ScholarPubMed
Cox, T. (2019). Muddling-through and deep learning for managing large-scale uncertain risks. Journal of Benefit-Cost Analysis, 10(2), 226–250. https://doi.org/10.1017/bca.2019.17.CrossRefGoogle Scholar
Craft, J., Halligan, J. (2020). Advising Governments in the Westminster Tradition: Policy Advisory Systems in Australia. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Craft, J., Head, B., Howlett, M. P. (2024). Expertise, policy advice, and policy advisory systems in an open, participatory, and populist era: New challenges to research and practice. Australian Journal of Public Administration, 83, 143–155. https://doi.org/10.1111/1467-8500.12630.CrossRefGoogle Scholar
Craft, J., Howlett, M. P. (2013). The dual dynamics of policy advisory systems: The impact of externalization and politicization on policy advice. Policy and Society, 32(3), 187–197. https://doi.org/10.1016/j.polsoc.2013.07.001.CrossRefGoogle Scholar
Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.Google Scholar
Cross, E., Ramsey, R. (2021). Minds meets machines: Towards a cognitive science of human-machine interactions. Trends in Cognitive Sciences, 25(3), 200–212. https://doi.org/10.1016/j.tics.2020.11.009.CrossRefGoogle Scholar
Cugurullo, F., Caprotti, F., Cook, M., et al. (2023). The rise of AI urbanism in post-smart cities: A critical commentary on urban artificial intelligence. Urban Studies, 61(6), 1168–1182. https://doi.org/10.1177/00420980231203386.CrossRefGoogle Scholar
Cugurullo, F., Xu, Y. (2025). When AIs become oracles: generative artificial intelligence, anticipatory urban governance, and the future of cities. Policy and Society, 44(1), 98–115. https://doi.org/10.1093/polsoc/puae025.CrossRefGoogle Scholar
Curtis, C., Gillespie, N., Lockey, S. (2023). AI-deploying organizations are key to addressing ‘perfect storm’ of AI risks. AI Ethics, 3, 145–153. https://doi.org/10.1007/s43681-022-00163-7.CrossRefGoogle ScholarPubMed
Das, D., Kwek, B. (2024). AI and data-driven urbanism: The Singapore experience. Digital Geography and Society, 7, 100104. https://doi.org/10.1016/j.diggeo.2024.100104.CrossRefGoogle Scholar
Daugherty, P., Wilson, H. (2018). Human + Machine: Reimagining Work in the Age of AI. Boston: Harvard Business Press.Google Scholar
De Leon, P. (1981). Policy sciences: Discipline and profession. Policy Sciences, 13(1), 1–7.Google Scholar
DeSouza, K. C., Jacob, B. (2017). Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043–1064. https://doi.org/10.1177/0095399714555751.CrossRefGoogle Scholar
Deutsch, K. W. (1963). The Nerves of Government. Models of Political Communication and Control. New York: Free Press.Google Scholar
Diakopoulos, N., Johnson, D. (2021). Anticipating and addressing the ethical implications of deepfakes in the context of elections. New Media & Society, 23(7), 2072–2098. https://doi.org/10.1177/1461444820925811.CrossRefGoogle Scholar
Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York: Basic Books.Google Scholar
Dunleavy, P. (2016). Big data and policy learning. Evidence-Based Policy Making in the Social Sciences. Methods That Matter. Edited by Stoker, Gerry, Evans, Mark. Chicago: Policy Press.Google Scholar
Dunleavy, P., Margetts, H. (2024). Data science, artificial intelligence and the third wave of digital era governance. Public Policy and Administration, 40(2), 185–214. https://doi.org/10.1177/09520767231198737.CrossRefGoogle Scholar
Dunlop, C. (2016). Knowledge, epistemic communities and agenda-setting, in Zahariadis, N. (ed), Routledge Handbook of Agenda-Setting. London: Routledge, 273–295.Google Scholar
Dunlop, C. A. (2017). The irony of epistemic learning: Epistemic communities, policy learning and the case of Europe’s hormones saga. Policy and Society, 36(2), 215–232. https://doi.org/10.1080/14494035.2017.1322260.CrossRefGoogle Scholar
Dunlop, C. A., Radaelli, C. M. (2020). Policy Learning in comparative policy analysis. Journal of Comparative Policy Analysis: Research and Practice, 24(1), 51–72. https://doi.org/10.1080/13876988.2020.1762077.Google Scholar
Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642.CrossRefGoogle Scholar
Eisenstein, J. (2019). Introduction to Natural Language Processing. Cambridge, MA: MIT Press.Google Scholar
Elmore, R. F. (1979). Backward mapping: Implementation research and policy decisions. Political Science Quarterly, 94(4), 601–616.CrossRefGoogle Scholar
Esko, T., Koulu, R. (2023). Imaginaries of better administration: Renegotiating the relationship between citizens and digital public power. Big Data & Society, 10(1), https://doi.org/10.1177/20539517231164113.CrossRefGoogle Scholar
Estlund, D. (2008). Democratic Authority: A Philosophical Framework. Princeton: Princeton University Press.Google Scholar
Estrada, M. A. R. (2011). Policy modeling: Definition, classification and evaluation. Journal of Policy Modeling, 33(4), 523–536. https://doi.org/10.1016/j.jpolmod.2011.02.003.CrossRefGoogle Scholar
Etzioni, A. (1968). The Active Society. A Theory of Societal and Political Process. New York: Free Press.Google Scholar
Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press.Google Scholar
Filgueiras, F. (2022a). Big data, artificial intelligence and the future of regulatory tools, in Howlett, M. P. (ed.), Routledge Handbook on Policy Tools. London: Routledge, pp. 529–539.Google Scholar
Filgueiras, F. (2022b). New Pythias of public administration: Ambiguity and choice in AI systems as challenges for governance. AI & Society, 37(4), 1473–1486. https://doi.org/10.1007/s00146-021-01201-4.CrossRefGoogle Scholar
Filgueiras, F., Almeida, V. (2021). Governance for the Digital World: Neither More State Nor More Market. London: Palgrave.CrossRefGoogle Scholar
Fischer, F. (2016) What is critical? Connecting the policy analysis to political critique. Critical Policy Studies, 10(1), 95–98. https://doi.org/10.1080/19460171.2015.1129350.CrossRefGoogle Scholar
Floridi, L. (2023a). AI as agency without intelligence: On ChatGPT, Large Language Models, and other generative models. Philosophy & Technology, 36(1), 15.CrossRefGoogle Scholar
Floridi, L. (2023b). The Ethics of Artificial Intelligence. Principles, Challenges and Opportunities. Oxford: Oxford University Press.CrossRefGoogle Scholar
Forestal, J. (2022). Designing for Democracy: How to Build Community in Digital Environments. Oxford: Oxford University Press.Google Scholar
Frischmann, B., Selinger, E. (2018). Re-engineering Humanity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds & Machines, 30, 411–437. https://doi.org/10.1007/s11023-020-09539-2.CrossRefGoogle Scholar
Gasser, U., Almeida, V. (2017). A layered model of AI goverance. IEEE Internet Computing, 21(6), 58–62. http://dx.doi.org/10.1109/MIC.2017.4180835.CrossRefGoogle Scholar
Ghosh, A., Saini, A., Barad, H. (2025). Artificial intelligence in governance: Recent trends, risks, challenges, innovative frameworks and future directions. AI & Society, early view, 1–23. https://doi.org/10.1007/s00146-025-02312-y.CrossRefGoogle Scholar
Giest, S. (2017). Big data for policymaking: fad or fasttrack? Policy Analysis, 50(3), 367–382. https://doi.org/10.1007/s11077-017-9293-1.Google Scholar
Goldman, A. I. (2003). The Technology and Economics of Communication, Knowledge in a Social World. Oxford: Oxford University Press.Google Scholar
Gyódi, K., Nawaro, Ł., Paliński, M., Wilamowski, M. (2023). Informing policy with text mining: Technological change and social challenges. Quality & Quantity, 57, 933–954. https://doi.org/10.1007/s11135-022-01378-w.CrossRefGoogle Scholar
Halligan, J. (1995). Policy advice and the public service, in Peters, B.G., Savoie, D. J. (eds.), Governance in a Changing Environment. Montreal: McGill-Queens’ University Press and Canadian Centre for Management Development, pp. 138–172.CrossRefGoogle Scholar
Hartley, K, Kuecker, G. D. (2022). Disrupted Governance: Towards a New Policy Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hashem, Y., Bright, J., Chakraborty, S., et al. (2025). Mapping the potential: Generative AI and public sector work. Using time use data to identify opportunities for AI adoption in Great Britain’s public sector. London: The Alan Turing Institute. www.turing.ac.uk/news/publications/mapping-potential-generative-ai-and-public-sector-work-using-time-use-data.Google Scholar
Heclo, H. (1974). Modern Social Politics in Britain and Sweden: From Relief to Income Maintenance. New Haven: Yale University Press.Google Scholar
Hellström, T., Jacob, M. (2017). Policy instrument affordances: a framework for analysis. Policy Studies, 38(6), 604–621. https://doi.org/10.1080/01442872.2017.1386442.CrossRefGoogle Scholar
Henman, P. (2018). Of algorithms, Apps and advice: Digital social policy and service delivery. Journal of Asian Public Policy, 12(1), 71–89. https://doi.org/10.1080/17516234.2018.1495885.CrossRefGoogle Scholar
Hentenryck, P. (2021). Machine learning for optimal power flows. Tutorials in Operations Research, 5(4), 62–82. https://doi.org/10.1287/educ.2021.0234.Google Scholar
Hjern, B., Porter, D. (1981). Implementation structures: A new unit of administrative analysis. Organizational Studies, 2(3), 211–227. https://doi.org/10.1177/017084068100200301.CrossRefGoogle Scholar
Hong, S. (2024). AI and bias, in Paul, R., Carmel, E., Cobbe, J. (eds.), Handbook on Public Policy and Artificial Intelligence. Cheltenham: Edward-Elgar, 109–122.CrossRefGoogle Scholar
Hood, C. C., Margetts, H. (2007). The Tools of Government in Digital Age. London: Palgrave MacMillan.CrossRefGoogle Scholar
Howlett, M. P. (2019). Policy design primer: Choosing the Right Tools for the Job. London: Routledge.CrossRefGoogle Scholar
Howlett, M. P. (2019). Procedural policy tools and the temporal dimensions of policy design. International Review of Public Policy, 1(1), 27–45. https://doi.org/10.4000/irpp.310.CrossRefGoogle Scholar
Howlett, M. P. (2015). Policy analytical capacity: The supply and demand for policy analysis in government. Policy and Society, 34(3–4), 173–182. http://dx.doi.org/10.1016/j.polsoc.2015.09.002.CrossRefGoogle Scholar
Howlett, M. P., Giest, S., Mukherjee, I., Taeihagh, A. (2025). New policy tools and traditional policy models: Better understanding behavioural, digital and collaborative instruments. Policy Design and Practice, 8(1), 121–137. https://doi.org/10.1080/25741292.2025.2495373.CrossRefGoogle Scholar
Howlett, M. P., Mukherjee, I., Rayner, J. (2018). Understanding policy designs over time. In Routledge Handbook of Policy Design, edited by Howlett, M.. London: Routledge.CrossRefGoogle Scholar
Howlett, M. P., Ramesh, M. (2003). Studying Public Policy. Policy Cycles and Policy Subsystems. Oxford: Oxford University Press.Google Scholar
Hurley, M. W., Wallace, W. A. (1986). Expert systems as decision aids for public managers: An assessment of the technology and prototyping as a design strategy. Public Administration Review, 46(4), 563–571.CrossRefGoogle Scholar
Ifenthaler, D., Majumdar, R., Gorissen, P. et al. (2024). Artificial intelligence in education: Implications for policymakers, researchers, and practitioners. Technology, Knowledge and Learning, early view, 1–18. https://doi.org/10.1007/s10758-024-09747-0.CrossRefGoogle Scholar
Imperial, M. (2021). Implementation structures: The use of top-down and bottom-up approaches to policy implementation. Oxford Research Encyclopedia – Politics. https://doi.org/10.1093/acrefore/9780190228637.013.1750.CrossRefGoogle Scholar
Innerarity, D. (2024). The epistemic impossibility of an artificial intelligence take-over of democracy. AI & Society, 39(4), 1667–1671. https://doi.org/10.1007/s00146-023-01632-1.CrossRefGoogle Scholar
Jacobs, J. (2024). The artificial intelligence shock and socio-political polarization. Technological Forecasting and Social Change, 199, 123006. https://doi.org/10.1016/j.techfore.2023.123006.CrossRefGoogle Scholar
Jaidka, K., Chen, T., Chesterman, S., et al. (2025). Misinformation, disinformation, and generative AI: Implications for perception and policy. Digital Government Research & Practice, 6(1), 1–15. https://doi.org/10.1145/3689372.CrossRefGoogle Scholar
Janssen, M., Helbig, N. (2018). Innovating and changing the policy-cycle: Policy-makers be prepared! Government Information Quarterly, 35(1), 99–105. https://doi.org/10.1016/j.giq.2015.11.009.CrossRefGoogle Scholar
Jarrahi, M. H., Lutz, C., Newlands, G. (2022). Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation. Big Data & Society, 9(2), 1–6. https://doi.org/10.1177/20539517221142824.CrossRefGoogle Scholar
Judge, B., Nitzberg, M, Russell, S. (2025). When code isn’t law: Rethinking regulation for artificial intelligence. Policy and Society, 44(1), 85–97. https://doi.org/10.1093/polsoc/puae020.CrossRefGoogle Scholar
Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475. https://doi.org/10.1257/000282803322655392.CrossRefGoogle Scholar
Kayser-Bril, N. (2020). In a quest to optimize welfare management, Denmark built a surveillance behemoth. Algorithmic Watch. Retrieved from: In a quest to optimize welfare management, Denmark built a surveillance behemoth.Google Scholar
Khanal, S., Zhang, H., Taeihagh, A. (2025). Why and how is the power of Big Tech increasing in the policy process? The case of generative AI. Policy and Society, 44(1), 52–69. https://doi.org/10.1093/polsoc/puae012.CrossRefGoogle Scholar
Kingdon, J. W. (1995). Agendas, Alternatives, and Public Policies. New York: Harper & Collins.Google Scholar
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Thousand Oaks: Sage.Google Scholar
Klijn, E. H., & Koppenjan, J. (2012). Governance network theory: Past, present, and future. Policy and Politics, 40(4), 587–606. https://doi.org/10.1332/030557312x655431.CrossRefGoogle Scholar
Knorr-Cetina, K. (1999). Epistemic Cultures: How the Sciences Make Knowledge. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Koga, N. M., Palotti, P. L. M, Pontes, P. A. M. M., Couto, B. G., Soares, M. L. V. (2023). Analytical capacity as a critical condition for responding to COVID-19 in Brazil. Policy and Society, 42(1), 117–130. https://doi.org/10.1093/polsoc/puac028.CrossRefGoogle Scholar
König, P. D., Wenzelburger, G. (2020). Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics. Government Information Quarterly, 37(3), 101489. https://doi.org/10.1016/j.giq.2020.101489.CrossRefGoogle Scholar
Korinek, A., Balwit, A. (2023). Aligned with whom? Direct and social goals and AI systems, in Bullock, J. B., Chen, Y. C., Himmelreich, J. et al. (eds.), The Oxford handbook of AI governance. Oxford: Oxford University Press, 65–85.CrossRefGoogle Scholar
Krishnamoorthy, C. S., Rajeev, S. (1996). Artificial Intelligence and Expert Systems for Engineers. Boca Raton: CRC Press.Google Scholar
Lazar, S. (2024). Automatic authorities: Power and AI, in Lane, M., Sethumadhavan, A. (eds.), Collaborative Intelligence: How Humans and AI are Transforming our World. Cambridge, MA: MIT Press, 32–45.Google Scholar
Landemore, H. (2012). Democratic Reason: Politics, Collective Intelligence, and the Rule of Many. Princeton: Princeton University Press.Google Scholar
Lascoumes, P., Le Galès, P. (2007). Understanding public policy thorough its instruments – From the nature of instruments to the sociology of public policy instrumentation. Governance, 20(1), 1–21. https://doi.org/10.1111/j.1468-0491.2007.00342.x.CrossRefGoogle Scholar
Lasswell, H. D. (1970). The emerging conception of policy sciences. Policy Sciences, 1(1), 3–14.CrossRefGoogle Scholar
LeCun, Y., Bengio, Y, Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539.CrossRefGoogle ScholarPubMed
Lehdonvirta, V. (2024). Cloud Empires: How Digital Platforms Are Overtaking the State and How We Can Regain Control. Cambridge, MA: MIT Press.Google Scholar
Lim, H. S. M., Taeihagh, A. (2019). Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 11(20), 5791. https://doi.org/10.3390/su11205791.CrossRefGoogle Scholar
Lin, T. C. W. (2017). The new market manipulation. Emory Law Journal, 66(6), 1253–1314.Google Scholar
Lindblom, C. E. (1959). The science of muddling through. Public Administration Review, 19(2), 79–88.CrossRefGoogle Scholar
Lipsky, M. (2010). Street-Level Bureaucracy: Dilemmas of the Individual in Public Service. New York: Russell Sage Foundation.Google Scholar
Liu, H. K., Tang, M., Collard, A. S. J. (2025). Hybrid intelligence for the public sector: A bibliometric analysis of artificial intelligence and crowd intelligence. Government Information Quarterly, 42(1), 102006. https://doi.org/10.1016/j.giq.2024.102006.CrossRefGoogle Scholar
Liu, M., Zhang, H., Sui, Y. (2024). Workplace artificial intelligence regulation in China: Between efficiency and social stability. ILR Review, 77 (5), 813–824. https://doi.org/10.1177/00197939241278956b.CrossRefGoogle Scholar
Logan, S. (2024). Tell me what you don’t know: Large language models and the pathologies of intelligence analysis. Australian Journal of International Affairs, 78(2), 220–228. https://doi.org/10.1080/10357718.2024.2331733.CrossRefGoogle Scholar
Lowi, T. (1964). American business, public policy, case-studies, and political theory. World Politics, 16(4), 677–715. https://doi.org/10.2307/2009452.CrossRefGoogle Scholar
Maragno, G., Tangi, L., Gastaldi, L., Benedetti, M. (2023). Exploring the factors, affordances and constraints outlining the implementation of artificial intelligence in public sector organizations. International Journal of Information Management, 73, 102686. https://doi.org/10.1016/j.ijinfomgt.2023.102686.CrossRefGoogle Scholar
Margetts, H., John, P. (2024). How rediscovering nodality can improve democratic governance in digital world. Public Administration, 102(3), 969–983. https://doi.org/10.1111/padm.12960.CrossRefGoogle Scholar
Margetts, H., Dorobantu, C. (2019). Rethink government with AI. Nature, 568(7751), 163–165.CrossRefGoogle ScholarPubMed
Markram, H. (2006). The Blue Brain Project. Nature Reviews Neuroscience, 17(2), 153–160. https://doi.org/10.1038/nrn1848.CrossRefGoogle Scholar
McCarthy, J. (1981). Epistemological problems of artificial intelligence, in Webber, B. L., Nilsson, N. I. (eds.), Readings in Artificial Intelligence. Berlin: Springer, pp. 459–465.CrossRefGoogle Scholar
Meijer, A., Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031–1039. https://doi.org/10.1080/01900692.2019.1575664.CrossRefGoogle Scholar
Mendonça, R. F., Filgueiras, F., Almeida, V. A. (2023). Algorithmic Institutionalism: The Changing Rules of Social + Political Life. Oxford: Oxford University Press.CrossRefGoogle Scholar
Mergel, I., Dickinson, H., Stenvall, J., Gasco, M. (2023). Implementing AI in the public sector. Public Management Review, early view, 1–14. https://doi.org/10.1080/14719037.2023.2231950.CrossRefGoogle Scholar
Mökander, J., Schroeder, R. (2024). Artificial Intelligence, rationalization, and the limits of control in the public sector: The case of tax policy optimization. Social Science Computer Review, 42(6), 1359–1378. https://doi.org/10.1177/08944393241235175.CrossRefGoogle Scholar
Moradi, M., Moradi, M., Bayat, F., Toosi, A. N. (2019). Collective hybrid intelligence: Towards a conceptual framework. International Journal of Crowd Science, 3(2), 198–220. https://doi.org/10.1108/IJCS-03-2019-0012.CrossRefGoogle Scholar
Muccione, V., Vaghefi, S. A., Bingler, J. et al. (2024). Integrating artificial intelligence with expert knowledge in global environmental assessments: Opportunities, challenges and the way ahead. Regional Environmental Change, 24(121), 1–8. https://doi.org/10.1007/s10113-024-02283-8.CrossRefGoogle Scholar
Neumann, O., Guirguis, K., Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685.CrossRefGoogle Scholar
Newman, J., Mintrom, M. (2023). Mapping the discourse on evidence-based policy, artificial intelligence, and the ethical practice of policy analysis. Journal of European Public Policy, 30(9),1839–1859. https://doi.org/10.1080/13501763.2023.2193223.CrossRefGoogle Scholar
Nobel, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.CrossRefGoogle Scholar
Nowak, A. J. (2022). Artificial intelligence in evidence-based medicine, in Lidströmer, N., Ashrafian, H. (eds.), Artificial Intelligence in Medicine. Berlin: Springer, 255–266. https://doi.org/10.1007/978-3-030-64573-1_43.CrossRefGoogle Scholar
Nowotny, H. (2021). In AI We Trust: Power, Illusion, and Control of Predictive Algorithms. New York: Polity Books.Google Scholar
Oder, N., Béland, D. (2025). Artificial intelligence, emotional labor, and the quest for sociological and political imagination among low-skilled workers. Policy and Society, 44(1), 116–128. https://doi.org/10.1093/polsoc/puae034.CrossRefGoogle Scholar
OECD. (2024). Governing with Artificial Intelligence: Are governments ready?, OECD Artificial Intelligence Papers, 20, OECD Publishing. https://doi.org/10.1787/26324bc2-en.Google Scholar
Olsen, J. P. (2006). Maybe it is time to rediscover bureaucracy. Journal of Public Administration Research and Theory, 16(1), 1–24. https://doi.org/10.1093/jopart/mui027.CrossRefGoogle Scholar
O’Neil, C. (2016). The Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.Google Scholar
Ostrom, E. (2005). Understanding Institutional Diversity. Princeton: Princeton University Press.Google Scholar
Padmaja, B., Moorthy, C. V. K. N. S. N., Venkateswarulu, N., Bala, M. M. (2023). Exploration of issues, challenges and latest developments in autonomous cars. Journal of Big Data, 10, 1–24. https://doi.org/10.1186/s40537-023-00701-y.CrossRefGoogle Scholar
Pattyn, V., Brans, M. (2015). Organisational analytical capacity: Policy evaluation in Belgium. Policy and Society, 34(3–4), 183–196. https://doi.org/10.1016/j.polsoc.2015.09.009.CrossRefGoogle Scholar
Paul, R. (2022). Can critical policy studies outsmart AI? Research agenda on artificial intelligence technologies and public policy. Critical Policy Studies, 16(4), 497–509. https://doi.org/10.1080/19460171.2022.2123018.CrossRefGoogle Scholar
Pedreschi, D., Pappalardo, L., Ferragina, E., et al. (2025). Human-AI coevolution. Artificial Intelligence, 339, 104244. https://doi.org/10.1016/j.artint.2024.104244.CrossRefGoogle Scholar
Peeters, R., Rentería, C., Cejudo, G. M. (2023). How information capacity shapes policy implementation: A comparison of administrative burdens in COVID-19 vaccination programs in the United States, Mexico, and the Netherlands. Government Information Quarterly, 40(4), 101871. https://doi.org/10.1016/j.giq.2023.101871.CrossRefGoogle Scholar
Peters, B. G., Pierre, J. (2016). Comparative Governance: Rediscovering the Functional Dimension of Governing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Peters, B. G. (2012). Information and governing: Cybernetic models of governance, in Levi-Faur, D. (ed.), The Oxford Handbook of Governance. Oxford: Oxford University Press, 113–128.Google Scholar
Pressman, J. L., Wildavsky, A. (1984). Implementation. Berkeley: University of California Press.Google Scholar
Ramezani, M., Takian, A., Bakhtiari, A. et al. (2023). The application of artificial intelligence in health policy: A scoping review. BMC Health Services Research, 23, 1416. https://doi.org/10.1186/s12913-023-10462-2.CrossRefGoogle ScholarPubMed
Reid, T., Gibert, J. (2022). Inclusion in human-machines interactions. Science, 375(6577), 149–150. https://doi.org/10.1126/science.abf2618.CrossRefGoogle Scholar
Russell, S. (2022). Why we need to regulate non-state use of arms. Forum Institutional. 18 May. www.weforum.org/agenda/2022/05/regulate-non-state-use-arms/#:~:text=Stuart%20Russell&text=An%20emerging%20arms%20race%20between,while%20the%20technology%20rapidly%20advances.Google Scholar
Russell, S. J. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. New York: Viking Books.Google Scholar
Russell, S. J., Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Englewood Cliffs: Prentice-Hall.Google Scholar
Sabatier, P. A. (1999). The need for better theories, in Sabatier, P. A. (ed.), Theories of the Policy Process. Boulder: Westview, pp. 3–17.Google Scholar
Sætra, H. S. (2023). The promise and pitfalls of techno-solutionism, in Technology and Sustainable Development: The Promises and Pitfalls of Techno-solutionism. London: Routledge, 1–9.CrossRefGoogle Scholar
Safaei, M., Longo, J. (2024). The end of policy analyst? Testing the capability of artificial intelligence to generate plausible, persuasive and useful policy analysis. Digital Government: Research and Practice, 5(1), 4–35. https://doi.org/10.1145/3604570.Google Scholar
Salamon, L. (2002). The tools of Government: A Guide to the New Governance. Oxford: Oxford University Press.Google Scholar
Samuel, A. L. (1962). Artificial intelligence: A frontier of automation. The Annals of the American Academy of Political and Social Science, 340(1), 10–20. https://doi.org/10.1177/000271626234000103.CrossRefGoogle Scholar
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1.2), 210–229. https://doi.org/10.1147/rd.33.0210.CrossRefGoogle Scholar
Schlosser, M. (2019). Agency, in Zalta, E. N. (ed.), The Stanford Encyclopedia of Philosophy. Stanford: Stanford University, 1–8. www.plato.stanford.edu/archives/win2019/entries/agency/.Google Scholar
Schuett, J. (2023). Risk management in the artificial intelligence act. European Journal of Risk Regulation, 15(2), 367–385. https://doi.org/10.1017/err.2023.1.CrossRefGoogle Scholar
Schwember, H. (1977). Cybernetics in government: Experience with new tools for management in Chile 1971–1973. In Bossel, H. (ed.), Concepts and Tools of Computer-Assisted Policy Analysis: Vol. 1: Basic Concepts. Birkhäuser: Basel, pp. 79–138.CrossRefGoogle Scholar
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. https://doi.org/10.1017/S0140525X00005756.CrossRefGoogle Scholar
Shardlow, M., Przybila, P. (2023). Deanthropomorphising NLP: Can a Language Model Be Conscious? ArXiv, 1–20. https://doi.org/10.48550/arXiv.2211.11483.CrossRefGoogle Scholar
Siddiki, S. (2020). Understanding and Analyzing Public Policy Design. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Silva, E. H. M., Santos, E. M. F., Miranda, M. L. B. M., Bezerra, S. L., Miranda, S. C. (2024). ChatTCU: Inteligência artificial como assistente do auditor. Revista do TCU, 153, 19–45.Google Scholar
Simon, H. A. (1995). Artificial intelligence: An empirical science. Artificial Intelligence, 77(1) 95–127.CrossRefGoogle Scholar
Simon, H. A., Eisenstadt, S. A. (2002). A Chinese room that understands, in Bishop, J. M., Preston, J. (eds.), Views into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford: Oxford University Press, 95–108.CrossRefGoogle Scholar
Song, S., Liu, X., Li, Y., Yu, Y. (2022). Pandemic policy assessment by artificial intelligence. Nature – Scientific Reports, 12, 13843. https://doi.org/10.1038/s41598-022-17892-8.CrossRefGoogle ScholarPubMed
Sorensen, E., Torfing, J. (2005). The democratic anchorage of governance networks. Scandinavian Political Studies, 28(3), 195–218. https://doi.org/10.1111/j.1467-9477.2005.00129.x.CrossRefGoogle Scholar
Simons, A., Voß, J. P. (2018). The concept of instrument constituencies: Accounting for dynamics and practices of knowing governance. Policy and Society, 37(1), 14–35. https://doi.org/10.1080/14494035.2017.1375248.CrossRefGoogle Scholar
Stoker, G. (1998). Governance as theory: Five propositions. International Social Science Journal, 50(155), 17–28. https://doi.org/10.1111/1468-2451.00106.CrossRefGoogle Scholar
Straub, V. J., Morgan, D., Bright, J., Margetts, H. (2023). Artificial intelligence in government: Concepts, standards, and a unified framework. Government Information Quarterly, 40(4), 101881. https://doi.org/10.1016/j.giq.2023.101881.CrossRefGoogle Scholar
Suchman, L. (2007). Human-Machine Reconfigurations: Plans and Situated Actions. Cambridge: Cambridge University Press.Google Scholar
Sun, T. Q., Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383. https://doi.org/10.1016/j.giq.2018.09.008.CrossRefGoogle Scholar
Süsser, D., Ceglarz, A., Gaschnig, H., et al. (2021). Model-based policymaking or policy-based modelling? How energy models and energy policy interact. Energy Research & Social Science, 75, 101984. https://doi.org/10.1016/j.erss.2021.101984.CrossRefGoogle Scholar
Sutton, R., Barto, A. (2018). Introduction to Reinforcement Learning. Cambridge, MA: MIT Press.Google Scholar
Taeihagh, A. (2025). Governance of generative AI. Policy and Society, 44(1), 1–22. https://doi.org/10.1093/polsoc/puaf001.CrossRefGoogle Scholar
Taeihagh, A., Ramesh, M., Howlett, M. (2021). Assessing the regulatory challenges of emerging disruptive technologies. Regulation & Governance, 15(4), 1009–1019. https://doi.org/10.1111/rego.12392.CrossRefGoogle Scholar
Tallberg, J., Erman, E., Furendal, M., et al. (2023). The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review, 25(3), viad040. https://doi.org/10.1093/isr/viad040.CrossRefGoogle Scholar
Tomasello, M. (2022). The Evolution of Agency: Behavioral Organizations from Lizards to Humans. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Turing, A. (1950). Computing Machinery and Intelligence. Mind—A Quarterly Review of Psychology and Philosophy, 59(236), 433–460.CrossRefGoogle Scholar
Turkle, S. (1984). The Second Self: Computers and the Human Spirit. New York: Simon & Schuster.Google Scholar
Tyler, C., Akerlof, K. L., Allegra, A., et al. (2023). AI tools as science policy advisers? The potential and the pitfalls. Nature, 622, 27–30. https://doi.org/10.1038/d41586-023-02999-3.CrossRefGoogle ScholarPubMed
Ulnicane, I. (2022). Artificial intelligence in the European Union, in Hoerber, T, Weber, G., Cabras, I. (eds.), The Routledge Handbook on European Integrations. London: Routledge, 95–108. https://doi.org/10.4324/9780429262081-19.Google Scholar
Ulnicane, I., Aden, A. (2023). Power and politics in framing bias in Artificial Intelligence policy. Review of Policy Research, 40(5), 665–687. https://doi.org/10.1111/ropr.12567.CrossRefGoogle Scholar
Urbina, F., Lentzos, F., Invernizzi, C., Ekins, S. (2022). Dual use of artificial-intelligence-powered drug discovery. Nature Machine Intelligence, 4, 189–191. https://doi.org/10.1038/s42256-022-00465-9.CrossRefGoogle ScholarPubMed
Valle-Cruz, D., Fernandez-Cortez, V., Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644. https://doi.org/10.1016/j.giq.2021.101644.CrossRefGoogle Scholar
Valle-Cruz, D., Sandoval-Almazán, R. (2022). Role and governance of artificial intelligence in the public policy cycle, in Bullock, Justin B. et al. (eds.), The Oxford Handbook of AI Governance. Oxford: Oxford University Press, 534–550.CrossRefGoogle Scholar
Valle-Cruz, D., Criado, J. I., Sandoval-Almazán, R., Ruvalcaba-Gomez, E. A. (2020). Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation. Government Information Quarterly, 37(4), 101509. https://doi.org/10.1016/j.giq.2020.101509.CrossRefGoogle Scholar
Van der Voort, H. G., Klievink, A. J., Arnaboldi, M., Meijer, A. J. (2019). Rationality and politics of algorithms: Will the promise of big data survive the dynamics of public decision making? Government Information Quarterly, 36(1), 27–38. https://doi.org/10.1016/j.giq.2018.10.011.CrossRefGoogle Scholar
van Prooijen, J. W., Šrol, J., Maglić, M. (2025). How belief in conspiracy theories could harm sustainability. Nature Human Behavior, early view. https://doi.org/10.1038/s41562-025-02243-0.CrossRefGoogle Scholar
Veale, M., Brass, I. (2019). Administration by algorithm? Public management meets public sector machine learning, in Yeung., K, Lodge, M. (eds), Algorithmic Regulation. Oxford: Oxford University Press, 121–149. https://doi.org/10.1093/oso/9780198838494.003.0006.CrossRefGoogle Scholar
Verhulst, S. G. (2018). Where and when AI and CI meet: Exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern. AI & Society, 33(2), 293–297. https://doi.org/10.1007/s00146-018-0830-z.CrossRefGoogle Scholar
Veselý, A. (2017). Policy advice as policy work: A conceptual framework for multi-level analysis. Policy Sciences, 50, 139–154. https://doi.org/10.1007/s11077-016-9255-z.CrossRefGoogle Scholar
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003.CrossRefGoogle Scholar
Voß, J.-P., Simons, A. (2014). Instrument constituencies and the supply side of policy innovation: The social life of emissions trading. Environmental Politics, 23(5), 735–754. https://doi.org/10.1080/09644016.2014.923625.CrossRefGoogle Scholar
Wang, P. (2019). On defining artificial intelligence. Journal of General Artificial Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002.CrossRefGoogle Scholar
Wang, H., Fu, T., Du, Y. et al. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620, 47–60. https://doi.org/10.1038/s41586-023-06221-2.CrossRefGoogle ScholarPubMed
Wani, A.K., Rahayu, F., Ben Amor, I. et al. (2024). Environmental resilience through artificial intelligence: Innovations in monitoring and management. Environmental Science Pollution Research, 31, 18379–18395. https://doi.org/10.1007/s11356-024-32404-z.CrossRefGoogle ScholarPubMed
Weiss, C. H. (1998). Have we learned anything new about the use of evaluation? American Journal of Evaluation, 19(1), 21–33. https://doi.org/10.1016/S1098-2140(99)80178-7.CrossRefGoogle Scholar
Weiss, C. H. (1979). Using Social Research in Public Policy Making. London: Lexington Books.Google Scholar
Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation. San Francisco: W. H. Freeman.Google Scholar
White House (2023). Executive order on the safe, secure, and trustworthy development and use of Artificial Intelligence. www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/?utm_source=link.Google Scholar
Willems, J., Schmidthuber, L., Vogel, D., Ebinger, F., & Vanderelst, D. (2022). Ethics of robotized public services: The role of robot design and its actions. Government Information Quarterly, 39(2). https://doi.org/10.1016/j.giq.2022.101683.CrossRefGoogle Scholar
Wilson, R. (2009). Policy analysis as policy advice, in Goodin, R. E., Moran, M., Rein, M. (eds.), The Oxford Handbook of Public Policy. Oxford University Press, 152–168.CrossRefGoogle Scholar
Wirtz, B. W., Weyerer, J. C., Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851.CrossRefGoogle Scholar
Xavier, B. (2025). Biases within AI: Challenging the illusion of neutrality. AI & Society, 40, 1545–1546. https://doi.org/10.1007/s00146-024-01985-1.CrossRefGoogle Scholar
Yang, Q., Wu, K., Jiang, Y. (2007). Learning action models from plan examples using weighted MAX-SAT. Artificial Intelligence, 171(2), 107–143. https://doi.org/10.1016/j.artint.2006.11.005.CrossRefGoogle Scholar
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12158.CrossRefGoogle Scholar
Yoshida, Y., Sitas, N., Mannetti, L., et al. (2024). Beyond academia: A case for reviews of gray literature for science-policy processes and applied research. Environmental Science & Policy, 162, 103882. https://doi.org/10.1016/j.envsci.2024.103882.CrossRefGoogle Scholar
Yuwono, E. I., Tjondronegoro, D., Riverola, C., Loy, J. (2024). Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions. Computers and Education: Artificial Intelligence, 7, 100272. https://doi.org/10.1016/j.caeai.2024.100272.Google Scholar
Zakharova, I., Jarke, J., Kaun, A. (2024). Tensions in digital welfare states: Three perspectives on care and control. Journal of Sociology, 60(3), 540–559.CrossRefGoogle Scholar
Zhou, Z. H. (2012). Ensemble Methods: Foundations and Algorithms. New York: CRC Press.CrossRefGoogle Scholar
Zhuo, H. H., Yang, Q., Hu, D. H., Li, L. (2010). Learning complex action models with quantifiers and logical implications. Artificial Intelligence, 174(18), 1540–1569. https://doi.org/10.1016/j.artint.2010.09.007.CrossRefGoogle Scholar
Accessibility standard: WCAG 2.1 AA
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The PDF of this Element
complies with version 2.1 of the
Web Content Accessibility Guidelines
(WCAG), covering newer accessibility requirements and improved
user experiences and achieves the intermediate
(AA)
level of WCAG compliance, covering a wider range of accessibility requirements.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visual Accessibility
Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Structural and Technical Features
ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.