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Bias on the web and beyond: an accessibility point of view

Published: 20 April 2020 Publication History

Abstract

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].

Reference

[1]
Ricardo Baeza-Yates. 2018. Bias on the Web. Commun. ACM 61, 6 (June 2018), 54--61.

Cited By

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  • (2024)Employing Hybrid AI Systems to Trace and Document Bias in ML PipelinesIEEE Access10.1109/ACCESS.2024.342738812(96821-96847)Online publication date: 2024
  • (2022)A Systematic Review of Web Accessibility MetricsApp and Website Accessibility Developments and Compliance Strategies10.4018/978-1-7998-7848-3.ch004(77-108)Online publication date: 2022

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cover image ACM Conferences
W4A '20: Proceedings of the 17th International Web for All Conference
April 2020
190 pages
ISBN:9781450370561
DOI:10.1145/3371300
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

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  1. accessibility
  2. bias

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  • Invited-talk

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W4A '20
W4A '20: 17th Web for All Conference
April 20 - 21, 2020
Taipei, Taiwan

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W4A '20 Paper Acceptance Rate 18 of 29 submissions, 62%;
Overall Acceptance Rate 171 of 371 submissions, 46%

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Cited By

View all
  • (2024)Employing Hybrid AI Systems to Trace and Document Bias in ML PipelinesIEEE Access10.1109/ACCESS.2024.342738812(96821-96847)Online publication date: 2024
  • (2022)A Systematic Review of Web Accessibility MetricsApp and Website Accessibility Developments and Compliance Strategies10.4018/978-1-7998-7848-3.ch004(77-108)Online publication date: 2022

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