Algorithmic empowerment: A comparative ethnography of two open-source algorithmic platforms – Decide Madrid and vTaiwan

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2022-07-01 DOI:10.1177/20539517221123505
Yu-Shan Tseng
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引用次数: 3

Abstract

Scholars of critical algorithmic studies, including those from geography, anthropology, Science and Technology Studies and communication studies, have begun to consider how algorithmic devices and platforms facilitate democratic practices. In this article, I draw on a comparative ethnography of two alternative open-source algorithmic platforms – Decide Madrid and vTaiwan – to consider how they are dynamically constituted by differing algorithmic–human relationships. I compare how different algorithmic–human relationships empower citizens to influence political decision-making through proposing, commenting, and voting on the urban issues that should receive political resources in Taipei and Madrid. I argue that algorithmic empowerment is an emerging process in which algorithmic–human relationships orient away from limitations and towards conditions of plurality, actionality, and power decentralisation. This argument frames algorithmic empowerment as bringing about empowering conditions that allow (underrepresented) individuals to shape policy-making and consider plural perspectives for political change and action, not as an outcome-driven, binary assessment (i.e. yes/no). This article contributes a novel, situated, and comparative conceptualisation of algorithmic empowerment that moves beyond technological determinism and universalism.
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算法赋能:两个开源算法平台的比较民族志——Decision Madrid和vTaiwan
批判性算法研究的学者,包括地理学、人类学、科学技术研究和传播学的学者,已经开始考虑算法设备和平台如何促进民主实践。在这篇文章中,我借鉴了两个替代开源算法平台——Decision Madrid和vTaiwan——的比较民族志,来考虑它们是如何由不同的算法-人类关系动态构成的。我比较了不同的算法-人际关系如何使公民能够通过对台北和马德里应该获得政治资源的城市问题提出建议、发表评论和投票来影响政治决策。我认为,算法赋权是一个新兴的过程,在这个过程中,算法与人的关系从局限性转向多元化、行动性和权力分散的条件。这一论点将算法赋权定义为带来赋权条件,允许(代表性不足的)个人制定政策,并考虑政治变革和行动的多元视角,而不是作为一种结果驱动的二元评估(即是/否)。本文提出了一个新颖的、情境化的、比较性的算法赋权概念,超越了技术决定论和普遍主义。
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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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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