Civicbase:一个用于部署调查研究的二次投票的开源平台

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2023-08-07 DOI:10.1002/aaai.12103
Madeline E. Bassetti, Gustavo Dias, Daniel L. Chen, Alan Mortoni, Ritesh Das
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引用次数: 0

摘要

公民参与正日益数字化。计算的普遍性增加了我们技术中介的互动。各国政府已开展各种数字化工作,以利用虚拟生活的这些新方面。还有待观察的是,能够为公共政策的制定和有效性提供信息的公民政治意见是否得到了准确的反映。Civicbase是一个开源的在线平台,支持研究二次投票调查(QVSR)的应用,这是一种新颖的调查方法。在本文中,我们探讨了QVSR作为一种获取政策偏好的有效方法、预测的最佳调查设计、Civicbase的功能和技术堆栈,以及个人人工智能这一新兴领域,及其与建模个人政治偏好的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Civicbase: An open-source platform for deploying Quadratic Voting for Survey Research

Civic engagement is increasingly becoming digital. The ubiquity of computing increases our technologically mediated interactions. Governments have instated various digitization efforts to harness these new facets of virtual life. What remains to be seen is if citizen political opinion, which can inform the inception and effectiveness of public policy, is being accurately captured. Civicbase is an open-source online platform that supports the application of Quadratic Voting Survey for Research (QVSR), a novel survey method. In this paper, we explore QVSR as an effective method for eliciting policy preferences, optimal survey design for prediction, Civicbase's functionalities and technology stack, and Personal AI, an emerging domain, and its relevance to modeling individual political preferences.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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