Citizen Participation and Machine Learning for a Better Democracy
- URL: http://arxiv.org/abs/2103.00508v1
- Date: Sun, 28 Feb 2021 13:30:07 GMT
- Title: Citizen Participation and Machine Learning for a Better Democracy
- Authors: M. Arana-Catania, F.A. Van Lier, Rob Procter, Nataliya Tkachenko,
Yulan He, Arkaitz Zubiaga, Maria Liakata
- Abstract summary: 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.
- Score: 22.682698778214434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project [0.0]
We review the Open AI Democratic Inputs programme, which funded 10 teams to design procedures for public participation in generative AI.
We identify several shared assumptions including the generality of LLMs, extracting abstract values, soliciting solutions not problems and equating participation with democracy.
We call instead for AI participation which involves specific communities and use cases and solicits concrete problems to be remedied.
arXiv Detail & Related papers (2024-07-16T11:22:34Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - What Can Natural Language Processing Do for Peer Review? [173.8912784451817]
In modern science, peer review is widely used, yet it is hard, time-consuming, and prone to error.
Since the artifacts involved in peer review are largely text-based, Natural Language Processing has great potential to improve reviewing.
We detail each step of the process from manuscript submission to camera-ready revision, and discuss the associated challenges and opportunities for NLP assistance.
arXiv Detail & Related papers (2024-05-10T16:06:43Z) - Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting [10.977733942901535]
Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities.
This work presents the results of behavioural experiments where participants were asked to vote in a fictional PB setting.
We identify approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process.
arXiv Detail & Related papers (2023-10-05T12:25:48Z) - Consensus-based Participatory Budgeting for Legitimacy: Decision Support
via Multi-agent Reinforcement Learning [3.3504365823045044]
Participatory budgeting is a process where voting outcomes may not always be fair or inclusive.
This paper introduces a novel and legitimate consensus-based participatory budgeting process.
Voters are assisted to interact with each other to make viable compromises.
arXiv Detail & Related papers (2023-07-24T16:16:23Z) - UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course.
We introduce UKP-SQuARE as a platform for QA education.
Students can run, compare, and analyze various QA models from different perspectives.
arXiv Detail & Related papers (2023-05-31T11:29:04Z) - Lessons Learned from a Citizen Science Project for Natural Language
Processing [53.48988266271858]
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
We conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset.
Our results show that this can yield high-quality annotations and attract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues.
arXiv Detail & Related papers (2023-04-25T14:08:53Z) - 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) - Neural Approaches to Conversational Information Retrieval [94.77863916314979]
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface.
Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI.
This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years.
arXiv Detail & Related papers (2022-01-13T19:04:59Z) - Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
arXiv Detail & Related papers (2021-10-05T12:51:11Z) - A mixed-methods ethnographic approach to participatory budgeting in
Scotland [11.943141057130228]
Participatory budgeting (PB) is already well established in Scotland in the form of community led grant-making.
This research paper explores how each of the 32 local authorities that make up Scotland utilise the Consul platform to engage their citizens in the PB process.
We focus on whether natural language processing (NLP) tools can facilitate both citizen engagement, and the processes by which citizens' contributions are analysed and translated into policies.
arXiv Detail & Related papers (2021-09-20T13:04:24Z)
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.