The importance of algorithm skills for informed Internet use

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Jonathan Gruber, E. Hargittai
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引用次数: 2

Abstract

Using the Internet means encountering algorithmic processes that influence what information a user sees or hears. Existing research has shown that people's algorithm skills vary considerably, that they develop individual theories to explain these processes, and that their online behavior can reflect these understandings. Yet, there is little research on how algorithm skills enable people to use algorithms to their own benefit and to avoid harms they may elicit. To fill this gap in the literature, we explore the extent to which people understand how the online systems and services they use may be influenced by personal data that algorithms know about them, and whether users change their behavior based on this understanding. Analyzing 83 in-depth interviews from five countries about people's experiences with researching and searching for products and services online, we show how being aware of personal data collection helps people understand algorithmic processes. However, this does not necessarily enable users to influence algorithmic output, because currently, options that help users control the level of customization they encounter online are limited. Besides the empirical contributions, we discuss research design implications based on the diversity of the sample and our findings for studying algorithm skills.
算法技能对知情互联网使用的重要性
使用互联网意味着遇到影响用户看到或听到的信息的算法过程。现有的研究表明,人们的算法技能差异很大,他们会发展出个人的理论来解释这些过程,他们的在线行为可以反映出这些理解。然而,关于算法技能如何使人们利用算法为自己谋利并避免可能引发的伤害的研究很少。为了填补文献中的这一空白,我们探讨了人们在多大程度上理解他们使用的在线系统和服务可能受到算法所知道的个人数据的影响,以及用户是否会根据这种理解改变他们的行为。我们分析了来自五个国家的83个深度访谈,内容涉及人们在网上研究和搜索产品和服务的经历,我们展示了了解个人数据收集如何帮助人们理解算法过程。然而,这并不一定使用户能够影响算法输出,因为目前,帮助用户控制他们在网上遇到的定制级别的选项是有限的。除了实证贡献之外,我们还讨论了基于样本多样性的研究设计含义以及我们研究算法技能的发现。
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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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