Assessment Method for Generative AI Technology in Foresight and Policy Design in Public Management: Expanding AI Trustability for Anticipatory Governance
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Abstract
Objective: to develop and evaluate a generative AI system prototype and assessment method that supports anticipatory governance by integrating foresight and policy design, enabling stakeholders to anticipate and proactively address emerging challenges in public policy. Methods: the study uses a design science research approach, combining institutional and explainable AI frameworks. It designs and assesses a generative AI prototype through three case scenarios focusing on environmental, electoral, and labor regulations, and expands results to an assessment protocol. Results: the analysis demonstrates the strengths and limitations of generative AI in AG systems. The study produces a systemic framework and an assessment protocol for evaluating AI’s role in augmenting AG capabilities, focusing on enhancing trust and reliability. Conclusions: the article’s main contribution is the proposed assessment protocol that contributes to both theory and practice by providing a replicable method for enhancing trustability in AI-driven AG. The findings support researchers and policymakers in reflecting on and utilizing responsible AI to navigate complex geopolitical, environmental, and societal challenges.
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Andrade, N. N. G., & Kontschieder, V. (2021). AI impact assessment: A policy prototyping experiment. SSRN Electronic Journal. https://dx.doi.org/10.2139/ssrn.3772500
Athaluri, S. A., Manthena, S. V., Kesapragada, V. S. R. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023). Exploring the boundaries of reality: Investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references. Cureus, 15(4), e37432. http://doi.org/10.7759/cureus.37432
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922
Bevolo, M., & Amati, F. (2020). The potential role of AI in anticipating futures from a design process perspective: From the reflexive description of “design” to a discussion of influences by the inclusion of AI in the futures research process. World Futures Review, 12(2), 198-218. https://doi.org/10.1177/1946756719897402
Boobier, T. (2018). The impact of AI on professions. In T. Boobier (Ed.), Advanced Analytics and AI: Impact, Implementation, and the Future of Work (pp. 117-154). Wiley.
Cao, L. (2022). Beyond AutoML: Mindful and actionable AI and AutoAI with mind and action. IEEE Intelligent Systems, 37(5), 6-18. https://doi.org/10.1109/MIS.2022.3207860
Coeckelbergh, M. (2023). Democracy, epistemic agency, and AI: Political epistemology in times of artificial intelligence. AI and Ethics, 3(4), 1341-1350. https://doi.org/10.1007/s43681-022-00239-4
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
Complementary Law no. 135 of June 4, 2010. (2010). Lei da Ficha Limpa. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp135.htm
Decree no. 10,222 of February 5, 2020. (2020). Estratégia Nacional de Segurança Cibernética. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/decreto/d10222.htm
Decree-Law no. 5,452 of May 1, 1943. (1943). Consolidação das Leis do Trabalho (CLT). Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805
Dohmatob, E., Feng, Y., Yang, P., Charton, F., & Kempe, J. (2024). A tale of tails: Model collapse as a change of scaling laws. arXiv preprint arXiv:2402.07043. https://doi.org/10.48550/arXiv.2402.07043
Emsley, R. (2023). ChatGPT: These are not hallucinations but fabrications and falsifications. Schizophr, 9, 52. https://doi.org/10.1038/s41537-023-00379-4
Farrow, E. (2020). Organisational artificial intelligence future scenarios: Futurists insights and implications for the organisational adaptation approach, leader and team. Journal of Futures Studies, 24(3), 1-15. https://jfsdigital.org/wp-content/uploads/2020/03/01-Farrow-Organisational-Artificial-Intelligence-Future-Scenarios-Ed.9.pdf
Fuerth, L. S. (2009). Foresight and anticipatory governance. Foresight, 11(4), 14-32. https://doi.org/10.1108/14636680910982412
Fuerth, L. S. (2012). The project on forward engagement. Anticipatory Governance: The Project on Forward Engagement. https://forwardengagement.org/
Georgieff, A., & Hyee, R. (2022). Artificial Intelligence and employment: New cross-country evidence. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.832736
Geurts, A., Gutknecht, R., Warnke, P., Goetheer, A., Schirrmeister, E., Bakker, B., & Meissner, S. (2022). New perspectives for data‐supported foresight: The hybrid AI‐expert approach. Futures & Foresight Science, 4(1), e99. https://doi.org/10.1002/ffo2.99
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. Journal of Strategic Information Systems, 29(2), 101614. https://doi.org/10.1016/j.jsis.2020.101614
Guston, D. H. (2014). Understanding “anticipatory governance.” Social Studies of Science, 44(2), 218-242. https://doi.org/10.1177/0306312713508669
Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial Intelligence (AI) or Intelligence Augmentation (IA): What is the future? AI (Switzerland), 1(2), 143-155. https://doi.org/10.3390/ai1020008
Heo, K., & Seo, Y. (2021). Anticipatory governance for newcomers: Lessons learned from the UK, the Netherlands, Finland, and Korea. European Journal of Futures Research, 9(1). https://doi.org/10.1186/s40309-021-00179-y
Hussain, M., Tapinos, E., & Knight, L. (2017). Scenario-driven roadmapping for technology foresight. Technological Forecasting and Social Change, 124, 160-177. https://doi.org/10.1016/j.techfore.2017.05.005
Islam, S. R., Eberle, W., Ghafoor, S. K., & Ahmed, M. (2021). Explainable Artificial Intelligence approaches: A survey. arXiv preprint. https://arxiv.org/abs/2101.09429
Jokinen, L., Balcom Raleigh, N. A., & Heikkilä, K. (2023). Futures literacy in collaborative foresight networks: advancing sustainable shipbuilding. European Journal of Futures Research, 11(1). https://doi.org/10.1186/s40309-023-00221-1
Kolliarakis, G., & Hermann, I (2020). Towards European Anticipatory Governance for Artificial Intelligence. DGAP. https://dgap.org/en/research/publications/towards-european-anticipatory-governance-artificial-intelligence
Kunseler, E. M., Tuinstra, W., Vasileiadou, E., & Petersen, A. C. (2015). The reflective futures practitioner: Balancing salience, credibility and legitimacy in generating foresight knowledge with stakeholders. Futures, 66, 1-12. https://doi.org/10.1016/j.futures.2014.10.006
Laux, J., Wachter, S., & Mittelstadt, B. (2024). Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18(1), 3-32. https://doi.org/10.1111/rego.12512
Law no. 4,737 of July 15, 1965. (1965). Código Eleitoral. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/leis/l4737.htm
Law no. 9,096 of September 19, 1995. (1995). Lei dos Partidos Políticos. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/leis/l9096.htm
Law no. 9,433 of January 8, 1997. (1997). Lei das Águas. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/leis/l9433.htm
Law no. 9,605 of February 12, 1998. (1998). Lei de Crimes Ambientais. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/leis/l9605.htm
Law no. 12,187 of December 29, 2009. (2009). Política Nacional sobre Mudança do Clima. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2009/lei/l12187.htm
Law no. 12,305 of August 2, 2010. (2010). Política Nacional de Resíduos Sólidos. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2010/lei/l12305.htm
Law no. 12,651 of May 25, 2012. (2012). Código Florestal. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12651.htm
Law no. 12,965 of April 23, 2014. (2014). Marco Civil da Internet. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2014/lei/l12965.htm
Law no. 13,467 July 13, 2017. (2017). Reforma Trabalhista. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2017/lei/l13467.htm
Law no. 13,709 of August 14, 2018. (2018). Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Del Ser, J., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., Jiang, R., Khosravi, H., Lecue, F., Malgieri, A. P., Samek, W., Schneider, J., Speith, T., & Stumpf, S. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106, 102301. https://doi.org/10.1016/j.inffus.2024.102301
Maffei, S., Leoni, F., & Villari, B. (2020). Data-driven anticipatory governance. Emerging scenarios in data for policy practices. Policy Design and Practice, 3(2), 123-134. https://doi.org/10.1080/25741292.2020.1763896
Mikalef, P., Conboy, K., Eriksson Lundström, J., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems, 31(3), 257-268. https://doi.org/10.1080/0960085X.2022.2026621
Mishra, A. (2023). AI Alignment and social choice: Fundamental limitations and policy implications. arXiv preprint. https://doi.org/10.48550/arXiv.2310.16048
Muiderman, K., Zurek, M., Vervoort, J., Gupta, A., Hasnain, S., & Driessen, P. (2022). The anticipatory governance of sustainability transformations: Hybrid approaches and dominant perspectives. Global Environmental Change, 73, 102452. https://doi.org/10.1016/j.gloenvcha.2021.102452
Myllyoja, J., Rilla, N., & Lima-Toivanen, M. (2022). Strengthening futures-oriented agenda for building innovation ecosystems. European Journal of Futures Research, 10(1). https://doi.org/10.1186/s40309-022-00211-9
Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023). An empirical survey on explainable AI technologies: Recent trends, cases, and categories from technical and application perspectives. Electronics, 12(5), 1092. https://doi.org/10.3390/electronics12051092
Oehmichen, J., Schult, A., & Dong, J. Q. (2023). Successfully organizing AI innovation through collaboration with startups. Mis Quarterly Executive, 22(1). https://aisel.aisnet.org/misqe/vol22/iss1/4
Ohta, H. (2020). The analysis of Japan’s energy and climate policy from the aspect of anticipatory governance. Energies, 13(19), 5153. https://doi.org/10.3390/en13195153
OpenAI. (2024). ChatGPT 4 . https://openai.com/pt-BR/
Panizzon, M., & Janissek-Muniz, R. (2025). Theoretical dimensions for integrating research on anticipatory governance, scientific foresight, and sustainable S&T public policy design. Technology in Society, 80, 102758. https://doi.org/10.1016/j.techsoc.2024.102758
Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414. https://doi.org/10.1016/j.bushor.2020.01.003
Pflanzer, M., Dubljević, V., Bauer, W. A., Orcutt, D., List, G., & Singh, M. P. (2023, August 1). Embedding AI in society: Ethics, policy, governance, and impacts. AI and Society. Springer Science and Business Media Deutschland GmbH.
Puaschunder, J. M. (2019). On Artificial Intelligence’s razor’s edge: On the future of democracy and society in the artificial age. Journal of Economics and Business, 2(1), 100-119. https://dx.doi.org/10.2139/ssrn.3297348
Radanliev, P., & Roure, D. (2023). Review of the state of the art in autonomous artificial intelligence. AI and Ethics, 3(2), 497-504. https://doi.org/10.1007/s43681-022-00176-2
Rojas, A., & Tuomi, A. (2022). Reimagining the sustainable social development of AI for the service sector: the role of startups. Journal of Ethics in Entrepreneurship and Technology, 2(1), 39-54. https://doi.org/10.1108/JEET-03-2022-0005
Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273. https://doi.org/10.1016/j.knosys.2023.110273
Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Artificial intelligence hallucinations. Critical Care, 27(1), 180. https://doi.org/10.1186/s13054-023-04473-y
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023). ChatGPT and other large language models are double-edged swords. Radiology, 307(2). https://doi.org/10.1148/radiol.230163
Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2023). The curse of recursion: Training on generated data makes models forget. arXiv preprint arXiv:2305.17493. https://doi.org/10.48550/arXiv.2305.17493
Srinivasan, V. (2022). AI & learning: A preferred future. Computers and Education: Artificial Intelligence, 3, 100062. https://doi.org/10.1016/j.caeai.2022.100062
Störmer, E., Bontoux, L., Krzysztofowicz, M., Florescu, E., Bock, A.-K., & Scapolo, F. (2020). Foresight – Using science and evidence to anticipate and shape the future. In V. Šucha & M. Sienkiewicz (Eds.), Science for Policy Handbook (pp. 128-142). Elsevier.
Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In ACM International Conference Proceeding. Association for Computing Machinery. https://doi.org/10.1145/3465416.3483305
Theunissen, M., & Browning, J. (2022). Putting explainable AI in context: Institutional explanations for medical AI. Ethics and Information Technology, 23. https://doi.org/10.1007/s10676-022-09649-8
Tolan, S., Pesole, A., Martinez-Plumed, F., Fernandez-Macias, E., Hernandez-Orallo, J., & Gomez, E. (2020). Measuring the occupational impact of AI: Tasks, cognitive abilities and AI benchmarks. Journal of Artificial Intelligence Research, 71, 191-236. https://doi.org/10.1613/jair.1.12647
Tyson, L. D., & Zysman, J. (2022). Automation, AI & Work. Daedalus, 151(2), 256-271. https://doi.org/10.1162/daed_a_01914
Van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714. https://doi.org/10.1016/j.giq.2022.101714
Van Woensel, L. (2020). Scientific foresight: Considering the future of science and technology. In St Antony’s Series. Palgrave Macmillan.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 2017-Dec). Neural information processing systems foundation.
Verma, S., & Singh, V. (2022). Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms. Computers in Human Behavior, 131, 107215. https://doi.org/10.1016/j.chb.2022.107215
Wright, G., O’Brien, F., Meadows, M., Tapinos, E., & Pyper, N. (2020). Scenario planning and foresight: Advancing theory and improving practice. Technological Forecasting and Social Change, 159, 120220. https://doi.org/10.1016/j.techfore.2020.120220
Yang, W., Wei, Y., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., Amin, M. B., & Kang, B. (2023). Survey on explainable AI: From approaches, limitations, and applications aspects. Human-Centric Intelligent Systems, 3, 161-188. https://doi.org/10.1007/s44230-023-00038-y