AI-enhanced Civic Monitoring for Transparent Policy Debates

Thesis Abstract

This study investigates how AI-enabled civic monitoring can enhance transparency in public policy debates by providing verifiable, real-time insights into policy discourse, stakeholder influence, and information integrity. The context arises from increasing digitalization of public deliberation and concerns about algorithmic bias, misinformation, and opaque decision processes that undermine citizen trust and democratic legitimacy. The aim is to develop and evaluate an AI-driven civic monitoring framework that aggregates, analyzes, and visualizes policy debates across multiple digital platforms to reveal discourse dynamics, participation equity, and evidence quality. The specific objectives are (1) to design an AI-enabled pipeline for collecting and harmonizing discourse data from parliamentary portals, official consultations, and social media; (2) to assess discourse quality, relevance, and sentiment using natural language processing, stance detection, and factuality evaluation guided by the Epistemic Vigilance Theory; (3) to quantify participation equity across demographic and interest groups through social network and event-based analysis; (4) to evaluate transparency and accountability by auditing policy recommendations, stakeholder submissions, and decision-trail traceability; (5) to develop policy-relevant dashboards that communicate findings to policymakers and the public; and (6) to provide governance recommendations for deploying AI-powered civic monitoring ethically and effectively. The methodology adopts a mixed-methods research design combining computational social science with qualitative inquiry. The population comprises online policy consultation spaces, legislative portals, and major municipal and national social media platforms over a 24-month period. A stratified sample of 2,000 policy documents, 1,500 stakeholder submissions, and 1,000,000 social media posts will be collected, with a purposive sub-sample of 300 high-stakes policy debates for in-depth analysis. Data collection will utilize web-scraping tools, official API streams, and document repositories, supplemented by expert-verified metadata. Instrumentation includes (a) an AI-driven content analytics toolkit for topic modeling (Latent Dirichlet Allocation), event detection, and multilingual named entity recognition; (b) a stance and factuality module employing transformer-based classifiers calibrated on a domain-specific reliability corpus; (c) a participation equity module using social network analysis metrics (betweenness, eigenvector centrality) and demographic inference with privacy-preserving methods; (d) an audit-log framework to ensure transparency and reproducibility of the monitoring process. Validation will employ triangulation across automated metrics and human coders, ensuring convergent validity through inter-coder reliability (Cohen’s kappa > 0.8 for coded samples). Analytical approaches include regression analyses to identify predictors of discourse quality and transparency, ANOVA to test differences across platform types and policy domains, thematic analysis of qualitative sub-samples to capture governance concerns, and structural equation modeling to examine relationships among AI-driven visibility, participation equity, and perceived legitimacy. A conceptual model integrating information quality, participation equity, and governance legitimacy anchors the analysis. Key expected findings include (i) evidence that AI-enhanced monitoring increases the visibility of minority and marginalized stakeholders in policy deliberations; (ii) identification of systematic biases in platform-specific discourse that influence policy outcomes; (iii) a measurable improvement in perceived transparency and trust in the policy-making process among surveyed citizens; (iv) robust dashboards that provide real-time anomaly detection for misinformation and manipulation attempts; and (v) governance requirements for ethical AI deployment, including data provenance, explainability, and accountability mechanisms. The study contributes to knowledge by bridging computational methods with political theory on deliberative quality and democratic accountability, operationalizing Epistemic Vigilance Theory in the digital policy arena, and offering an empirically validated framework for AI-assisted civic monitoring that can be adapted across jurisdictions. Implications include informing parliamentary practices on openness, shaping civil society strategies for constructive engagement, and guiding policymakers in designing transparent decision-making workflows. The main conclusion anticipates that AI-enhanced civic monitoring can substantively elevate transparency and legitimacy in policy debates when implemented with rigorous governance safeguards. Recommendations emphasize transparent data sources, continuous model auditing, stakeholder-inclusive governance structures, and capacity-building for public institutions to integrate AI-enabled monitoring into routine policy processes.