From Participation to Influence: AI-Mediated Citizen Voice in Public Policymaking

Artificial intelligence is increasingly being incorporated into public participation and policymaking processes, where it can classify, summarize, aggregate, translate, and otherwise process citizen contributions (OECD, 2026). Recent research demonstrates how AI and machine-learning systems can process large volumes of citizen input, support participatory budgeting, and facilitate the integration of citizen contributions into policymaking processes (Shin, 2025). At the same time, emerging scholarship conceptualizes AI as a form of mediation that can reshape how citizen preferences are aggregated, interpreted, and transmitted to political representatives and public institutions (Rymon, 2026; Doudoute & Garno, 2026). However, existing reviews have primarily mapped the use of AI in e-Participation or examined broader forms of civic participation in relation to AI, with limited attention to the question of citizen influence on subsequent policymaking (Vasilakopoulos et al., 2024). This paper therefore examines how AI-mediated participation shapes the relationship between citizen voice and public policymaking. The paper conducts a systematic literature review (SLR) of empirical research on AI-supported participation within public policymaking and related public decision-making processes. Following the PRISMA framework, the study will search Scopus and Web of Science using combinations of terms relating to artificial intelligence and algorithms, citizen and public participation, and policymaking, governance, and public decision-making. The review will include studies examining AI-mediated processes through which citizen input is processed, filtered, aggregated, interpreted, prioritized, or transmitted within a public policymaking or public decision-making process. The paper aims to make three contributions. First, it extends existing research on AI-supported participation by systematically synthesizing empirical evidence concerning the relationship between citizen input and subsequent public policymaking, rather than focusing primarily on the adoption, functionality, or potential benefits of AI-enabled participation tools. Second, it conceptualizes AI as an algorithmic intermediary of citizen voice, highlighting that the democratic significance of AI lies not only in enabling participation but also in shaping what policymakers see, how citizen contributions are interpreted, and which voices become salient. Third, it develops an analytical framework for distinguishing increases in the quantity or efficiency of participation from meaningful changes in citizen influence. References Doudoute, N., & Garno, Z. (2026). AI-mediated participation in the digital public sphere: Democratic opportunities, algorithmic risks, and governance conditions. International Journal of Public Administration in the Digital Age, 13(1), 1–24. https://doi.org/10.4018/IJPADA.408841 OECD. (2026). Artificial intelligence and the future of citizen participation: Typology of applications, opportunities and challenges for democratic innovation. OECD Public Governance Reviews. OECD Publishing. https://doi.org/10.1787/a1ee2e0a-en Rymon, Y. (2026). Of the people, by the algorithm: How AI transforms the role of democratic representatives? AI & Society, 41, 5997–6012. https://doi.org/10.1007/s00146-026-02852-x Shin, B. (2025). Exploring the potential of machine learning to reduce administrative burden in participatory budgeting: A case study of Seoul. Journal of Public Budgeting, Accounting & Financial Management, 38(1), 237–264. https://doi.org/10.1108/JPBAFM-09-2024-0188 Vasilakopoulos, Z., Tavantzis, T., Promikyridis, R., & Tambouris, E. (2024). The use of artificial intelligence in eParticipation: Mapping current research. Future Internet, 16(6), 198-214. https://doi.org/10.3390/fi16060198