Towards Effective EU E-Participation: The Development of AskThePublic

• URL: http://arxiv.org/abs/2504.03287v1
• Date: Fri, 04 Apr 2025 09:15:06 GMT
• Title: Towards Effective EU E-Participation: The Development of AskThePublic
• Authors: Kilian Sprenkamp, Nils Messerschmidt, Amir Sartipi, Igor Tchappi, Xiaohui Wu, Liudmila Zavolokina, Gilbert Fridgen,
• Abstract summary: E-participation platforms can be an important asset for governments in increasing trust and fostering democratic societies.<n>We apply the Media Richness Theory and apply the Design Science Research method to create AskThePublic.<n>The results show that the participants value the interactive and structured responses as well as enhanced language capabilities.
• Score: 2.6515252450543025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: E-participation platforms can be an important asset for governments in increasing trust and fostering democratic societies. By engaging non-governmental and private institutions, domain experts, and even the general public, policymakers can make informed and inclusive decisions. Drawing on the Media Richness Theory and applying the Design Science Research method, we explore how a chatbot can be designed to improve the effectiveness of the policy-making process of existing citizen involvement platforms. Leveraging the Have Your Say platform, which solicits feedback on European Commission initiatives and regulations, a Large Language Model based chatbot, called AskThePublic is created, providing policymakers, journalists, researchers, and interested citizens with a convenient channel to explore and engage with public input. By conducting 11 semistructured interviews, the results show that the participants value the interactive and structured responses as well as enhanced language capabilities, thus increasing their likelihood of engaging with AskThePublic over the existing platform. An outlook for future iterations is provided and discussed with regard to the perspectives of the different stakeholders.

Related papers

• Improving municipal responsiveness through AI-powered image analysis in E-Government [0.0]
  This article explores the innovative application of Machine Learning (ML) for image analysis. By using image classification and object detection algorithms, the model supports public institutions in identifying and fast responding to evidence submitted by citizens.
  arXiv  Detail & Related papers  (2025-04-11T20:53:23Z)
• Applications of Artificial Intelligence Tools to Enhance Legislative Engagement: Case Studies from Make.Org and MAPLE [0.0]
  This paper is a collaboration between Make.org and the Massachusetts Platform for Legislative Engagement (MAPLE)<n>Make.org is developing massive online participative platforms that can engage hundreds of thousands or even millions of participants.<n>We believe that assistive integrations of AI can meaningfully impact the equity, efficiency, and accessibility of democratic legislating.
  arXiv  Detail & Related papers  (2025-02-12T19:52:15Z)
• Modular Conversational Agents for Surveys and Interviews [6.019313905775819]
  This paper introduces a modular approach and its resulting parameterized process for designing AI agents.<n>We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies.<n>The results suggest that the AI agent increases completion rates and response quality.
  arXiv  Detail & Related papers  (2024-12-22T15:00:16Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
  This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
  arXiv  Detail & Related papers  (2024-09-15T03:17:38Z)
• Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation [1.2699007098398807]
  We present an extension of adhocracy+, a large-scale open source participation platform, that provides two additional debate modules. These modules are supported by AI to enhance the discussion quality and participant interaction.
  arXiv  Detail & Related papers  (2024-09-12T06:27:35Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces [55.00702535694059]
  KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI.
  arXiv  Detail & Related papers  (2024-04-22T15:01:39Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Application of Text Analytics in Public Service Co-Creation: Literature Review and Research Framework [68.8204255655161]
  An alternative to the traditional top-down development of public services is co-creation of public services. Co-creation promotes collaboration between stakeholders with the aim to create better public services and achieve public values. We study existing works on the application of Text Analytics (TA) techniques on text data to support public service co-creation.
  arXiv  Detail & Related papers  (2023-05-20T17:34:17Z)
• Beyond Transactional Democracy: A Study of Civic Tech in Canada [3.2814818900171763]
  Civic tech groups organize around issues of shared concern to explore new forms of democratic technologies. This paper explores how a Civic Tech Toronto creates a platform for civic engagement through the maintenance of an autonomous community. The case shows that understanding civic tech requires a lens beyond the mere analysis or production of technical artifacts.
  arXiv  Detail & Related papers  (2023-02-13T19:31:13Z)
• Citizen Participation and Machine Learning for a Better Democracy [22.682698778214434]
  We report on the progress of a project that aims to address barriers to 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.
  arXiv  Detail & Related papers  (2021-02-28T13:30:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.