偏见在网络和超越:可访问性的观点

R. Baeza-Yates
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引用次数: 1

摘要

Web是人类创造的最强大的通信媒介和最大的公共数据存储库。它的内容从维基百科(Wikipedia)等重要参考来源到丑陋的假新闻都有。事实上,社交(数字)媒体只是我们自己的一面放大镜,无论好坏。事实上,所有人都有自己的文化和认知偏见(例如,确认偏见,见图1),网络内容以及我们的网络互动都受到它们的污染。数据偏差包括冗余和垃圾邮件,而交互偏差包括活动和呈现/曝光偏差。此外,有时算法会增加偏见,特别是在搜索和推荐系统的背景下。由于偏见会产生偏见,我们强调消除偏见数据的重要性,以及使用上下文和其他技术(如探索和利用)来打破过滤气泡。我们的主要目标是让人们意识到在网络上影响我们所有人的不同偏见,并强调我们应该设计包容性的内容,这样我们就可以帮助有学习障碍(例如,阅读障碍)或视力问题(例如,道尔顿症)等的人。这使得所有人都更容易访问Web。最后,我们必须指出,意识是能够对抗和减少网络偏见恶性循环的第一步。要了解更多细节,请参阅我关于这个主题的文章[1]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias on the web and beyond: an accessibility point of view
The Web is the most powerful communication medium and the largest public data repository that humankind has created. Its content ranges from great reference sources such as Wikipedia to ugly fake news. Indeed, social (digital) media is just an amplifying mirror of ourselves, good or bad. Indeed, as all people has their own cultural and cognitive biases (e.g., confirmation bias, see Figure 1), web content as well as our web interactions are tainted with them. Data bias includes redundancy and spam, while interaction bias includes activity and presentation/exposure bias. In addition, sometimes algorithms add bias, particularly in the context of search and recommendation systems. As bias generates bias, we stress the importance of debiasing data as well as using the context and other techniques such as explore & exploit, to break filter bubbles. Our main goal is to make people aware of the different biases that affect all of us on the Web as well as stress that we should design inclusive content such that we help people with learning disabilities (e.g., dyslexia) or vision problems (e.g., daltonism), among others. This makes the Web more accessible for all people. Finally, we have to remark that awareness is the first step to be able to fight and reduce the vicious cycle of web bias. For more details see my article on this topic [1].
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