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You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation
journal contribution
posted on 2020-06-21, 01:02 authored by C Lai, Markus Luczak-RoeschMarkus Luczak-Roesch© 2019, Springer Nature Switzerland AG. The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to$$20\%$$ of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.
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Preferred citation
Lai, C. & Luczak-Roesch, M. (2019). You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11864 LNCS, 253-266. https://doi.org/10.1007/978-3-030-34971-4_17Publisher DOI
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume
11864 LNCSPublication date
2019-01-01Pagination
253-266Publisher
Springer International PublishingPublication status
PublishedOnline publication date
2019-11-11ISSN
0302-9743eISSN
1611-3349Usage metrics
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