APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE IN THE CREATION OF POLITICAL SPEECHES IN THE PARLIAMENTARY ENVIRONMENT

Authors

DOI:

https://doi.org/10.55028/xdgjcm72

Keywords:

Decision Making, Generative Artificial Intelligence, Parliament, Canabidiol

Abstract

This study investigates the application of Generative Artificial Intelligence (GenAI) in the parliamentary environment, aiming to improve decision-making for public policy formulation. It explores how large language models (LLMs) can be used to generate political speeches that support legislative debates and decisions while maintaining acceptable standards of information quality. Regarding design, methodology, and approach, the research was structured around data selection and preparation, LLM/GenAI model implementation, and result verification and validation. The legislative proposal selected for the study was the regulation of Cannabidiol use; this was analyzed based on AI-generated speeches that were subsequently reviewed by legislative technical support specialists. The findings reveal that GenAI can assist in processing, organizing, and analyzing vast amounts of information within Parliament, making it more accessible and understandable. A practical implication is that the use of GenAI can facilitate the automation of legislative tasks, thereby freeing up time for aides and lawmakers to focus on more strategic functions. In terms of originality and value, the research contributes to the literature and to the specific applicability of AI within the legislative context. While existing literature largely focuses on theoretical aspects and general AI applications, this study centers on its specific, practical application within the parliamentary environment.

References

AGOSTINI, A. L. C. A inteligência artificial no poder público. In: SEMINÁRIO INTERNACIONAL- DEMOCRACIA E CONSTITUCIONALISMO, 13., 2020, Itajaí. Anais [...]. Itajaí: Universidade do Vale do Itajaí, 2020.

AHMET, Efe. The Impact of Artificial Intelligence on Social Problems and Solutions: An Analysis on The Context of Digital Divide and Exploitation. Yeni Medya Elektronik Dergi, v. 6, n. 2, p. 504-517, 2022. DOI: 10.55609/yenimedya.1146586.

ANDROUTSOPOULOU, A.; KARACAPILIDIS, N.; LOUKIS, E.; CHARALABIDIS, Y. Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, v. 36, n. 2, p. 358-367, 2019.

ASSEMBLEIA LEGISLATIVA DO ESTADO DE SANTA CATARINA (ALESC). Tramitações da proposição zeVEK. 2023. Disponível em: https://portalelegis.alesc.sc.gov.br/proposicoes/zeVEK/tramitacoes. Acesso em: 7 abr. 2024.

BESLEY, T.; PRESTON, I. Accountability and political competition: Theory and evidence. Cambridge: Weather Center for International Affairs, Harvard University, 2002.

CÂMARA DOS DEPUTADOS. Consultoria Legislativa da Câmara utiliza Inteligência Artificial para agilizar trabalhos. Portal da Câmara dos Deputados, 9 ago. 2019. Disponível em: https://www.camara.leg.br/assessoria-de-imprensa/568452-consultoria-legislativa-da-camara-utiliza-inteligencia-artificial-para-agilizar-trabalhos/. Acesso em: 4 abr. 2024.

CHOWDHURY, S. et al. Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, v. 33, n. 1, p. 100899, 2023.

COOMBS, C. et al. What is it about humanity that we can’t give away to intelligent machines? A European perspective. International Journal of Information Management, v. 58, p. 102311, 2021.

CREWE, E.; TAYLOR-ROBINSON, M. M.; MARTIN, S. The Future of Parliamentary and Legislative Studies. Parliamentary Affairs, v. 75, n. 4, p. 754-766, 2022.

DAVENPORT, T. H.; RONANKI, R. Artificial intelligence for the real world. Harvard business review, v. 96, n. 1, p. 108-116, 2018.

DE ANGELIS, L. et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Frontiers in Public Health, v. 11, p. 1166120, 2023.

DORREN, L.; WOLF, E. E. How evidence-based policymaking helps and hinders policy conflict. Policy & Politics, v. 51, n. 3, p. 486-507, 2023.

DREHER, A.; LANG, V. F.; RICHERT, K. A economia política dos empréstimos da Corporação Financeira Internacional. Journal of Development Economics, v. 140, p. 242-254, 2019.

EBERL, J. M.; HUBER, R. A.; MEDE, N. G.; GREUSSING, E. Populist attitudes towards politics and science: how do they differ?. Political Research Exchange, v. 5, n. 1, p. 2159847, 2023.

ELOUNDOU, T.; MANNING, S.; MISHKIN, P.; ROCK, D. Gpts are gpts: An early look at the labor market impact potential of large language models. arXiv, 2303.10130, 2023.

ENHOLM, I. M.; PAPAGIANNIDIS, E.; MIKALEF, P.; KROGSTIE, J. Artificial intelligence and business value: A literature review. Information Systems Frontiers, v. 24, n. 5, p. 1709-1734, 2022.

FILGUEIRAS, F. Running for artificial intelligence policy in G20 Countries-Policy instruments and mixes matters? Revista Brasileira de Inovação, v. 21, e022016, 2022.

FLORIDI, L.; CHIRIATTI, M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, v. 30, n. 4, p. 681-694, 2020.

GIL, O.; CORTÉS-CEDIEL, M.; CANTADOR, I. Citizen Participation and the Rise of Digital Media Platforms in Smart Governance and Smart Cities. International Journal of E-Planning Research, v. 8, p. 19-34, 2019. DOI: 10.4018/IJEPR.2019010102.

HERTEL-FERNANDEZ, A.; MILDENBERGER, M.; STOKES, L. C. Legislative Staff and Representation in Congress. American Political Science Review, v. 113, n. 3, p. 622-636, 2019.

HOSSIN, M. A. et al. Big Data-Driven Public Policy Decisions: Transformation Toward Smart Governance. Sage Open, v. 13, n. 4, 2023. DOI: 10.1177/21582440231215123.

HOVY, E.; LAVID, J. Towards a ‘science’ of corpus annotation: a new methodological challenge for corpus linguistics. International journal of translation, v. 22, n. 1, p. 13-36, 2010.

IBEH, Chidera Victoria; ADEGBOLA, Ayodeji. AI and Machine Learning for Sustainable Energy: predictive modelling, optimization and socioeconomic impact in the usa. International Journal Of Applied Sciences And Radiation Research, [S.L.], v. 2, n. 1, p. 23-32, 14 mar. 2025. Iskender AKKURT. http://dx.doi.org/10.22399/ijasrar.19.

KAHNEMAN, D.; TVERSKY, A. Prospect theory: An analysis of decision under risk. In: LEE, C. F.; LEE, A. C. (Ed.). Handbook of the fundamentals of financial decision making: Part I. Singapore: World Scientific Publishing, 2013. p. 99-127.

KORYZIS, D. et al. Disruptive technologies for parliaments: A literature review. Future Internet, v. 15, n. 2, p. 66, 2023.

LINDBLOM, C. E. The science of “muddling through”. Public Administration Review, v. 19, n. 2, p. 79-88, 1959.

MATHEUS, R.; JANSSEN, M.; JANOWSKI, T. Design principles for creating digital transparency in government. Government Information Quarterly, v. 38, n. 1, p. 101550, 2021.

MELLADO, B. et al. Leveraging artificial intelligence and big data to optimize COVID-19 clinical public health and vaccination roll-out strategies in Africa. International Journal of Environmental Research and Public Health, v. 18, n. 15, p. 7890, 2021.

MILLER, H. The multiple dimensions of information quality. Information Systems Management, v. 13, n. 2, p. 79-82, 1996. DOI: 10.1080/10580539608906992.

MINTZBERG, H.; RAISINGHANI, D.; THÉORÊT, A. The Structure of “Unstructured” Decision Processes. Administrative Science Quarterly, v. 21, n. 2, p. 246-275, 1976.

NAVEED, H. et al. A comprehensive overview of large language models. arXiv, 2307.06435, 2023.

OOI, K. B. et al. The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, p. 1-32, 2023.

PAN, Y.; OKAZAKI, N.; INUI, K. Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. Journal of Artificial Intelligence Research, v. 67, p. 569-606, 2020.

RAMOS, A. S. M. Inteligência Artificial Generativa baseada em grandes modelos de linguagem: ferramentas de uso na pesquisa acadêmica. SciELO Preprints, 2023. DOI: 10.1590/scielopreprints.6105.

SIMON, H. A. Models of Man: Social and Rational; Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley, 1957.

ULNICANE, I. Artificial Intelligence in the European Union: Policy, ethics and regulation. In: CHRISTIANSEN, T.; VANHOONACKER, S. (Ed.). The Routledge handbook of European integrations. London: Taylor & Francis, 2022.

WAMBA-TAGUIMDJE, S. L. et al. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, v. 26, n. 7, p. 1893-1924, 2020. DOI: 10.1108/BPMJ-10-2019-0411.

WELTER, M.; CORRÊA, L.; GONÇALVES, A. Análise de agrupamentos em discursos políticos no parlamento. In: INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT (CONTECSI), 17., 2020, São Paulo. Anais [...]. São Paulo: TECSI-FEA-USP, 2020.

WELTER, M.; GONÇALVES, A.; ALVES, J. B. Networks Analysis in Topics Clusters of Political Speech in Parliament. In: INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT (CONTECSI), 18., 2021, Evento virtual. Anais [...]. São Paulo: TECSI-FEA-USP, 2021.

Published

2026-09-24

Issue

Section

Artigos