Citizen Participation and Machine Learning for a Better Democracy

Abstract

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens? experience of digital citizen participation platforms. Taking as a case study the ?Decide Madrid? Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

Governing Ethical AI Transformation: A Case Study of AuroraAI

“…Such an information and knowledge infrastructure and “collective-centricity” for the development of networked democracy could accelerate discussions and debates to ensure that they are more meaningful and lead to collective decisions that form the basis for action strategies among multi-level groups of people (Raikov, 2018 ). There is already evidence that AI-labeled technologies, in combination with other information and communication technologies (ICTs), can promote deliberative and participatory decision-making (Savaget et al, 2019 ; Arana-Catania et al, 2021 ). AI tools can potentially improve democratic processes and increase democratic responsiveness and accountability if they are aligned with social and political changes and values that support change (König and Wenzelburger, 2020 ).…”

Framework for Federated Learning Open Models in e-Government Applications

“…It deals with Natural Language Processing and text mining, which includes techniques like arguments, topics and rules extraction, clustering, similarity check, and sentiment analysis. This could be further applied to comments or whole texts in several domains like legal texts [15], consultation platforms [16], and social media [17] enhancing the democratic process through participation and better interpretation of the results or finding contradictions in a specific legal system. Furthermore, they are used to classify news [18] or detect fake news [19,20].…”

Learning through online participation: A longitudinal analysis of participatory budgeting using Big Data indicators

“…To our knowledge, Big Data analytics has mostly been used to explore the black box of digital platforms, particularly discussion quality and communicative processes. For instance, recent studies have applied automated computational methods, including unsupervised (e.g., topic modeling) or supervised machine learning techniques (e.g., random forests), within a broad family of natural language processing to assess the content quality of large-scale online discussion data (Arana-Catania et al, 2021;Beauchamp, 2018;Fournier-Tombs & di Marzo Serugendo, 2019;Fournier-Tombs & MacKenzie, 2021;Parkinson et al, 2020) or in combination with network analysis to assess its network structures (Aragón et al, 2017;Choi, 2014;Gonzalez-Bailon et al, 2010) Given that emphasis has been placed on content quality, our contribution is to assess dynamic interactions in online deliberation (Shin & Rask, 2021). Our indicators are based on Fishkin's (2009) two democratic dimensions -participation and deliberation -where equality serves as an overarching value.…”