Cited By
View all- Jindal PGouri H(2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
Personalization, Bias and Privacy
Pages 311 - 312
Abstract
Personalization can be seen as a positive bias towards each user. However, it also has negative consequences such as privacy loss as well as the filter bubble or echo chamber effect due to the feedback-loop that creates. In addition, the web system itself can bias the user interaction distorting the data used for personalization. In this presentation we discuss the interaction of these three elements: personalization, bias and privacy.
References
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Ricardo Baeza-Yates. 2018. Bias on the Web. Communications of ACM 61, 6 (June), 54--61.
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Ricardo Baeza-Yates. 2018. Re-examining User Experience: Can Personalization and Privacy Coexist. TechTalks, https://bdtechtalks.com/2018/08/10/personalization-vs-privacy-understanding-the-tradeoff/
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Jakob Nielsen. 2006. The 90--9--1 Rule for Participation Inequality in Social Media and Online Communities. Nielsen Group, https://www.nngroup.com/articles/participation-inequality/.
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Index Terms
- Personalization, Bias and Privacy
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July 2020
395 pages
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Association for Computing Machinery
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Published: 13 July 2020
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UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization
July 14 - 17, 2020
Genoa, Italy
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View all- Jindal PGouri H(2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
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