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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 21.06.2020 by C Lai, Markus 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.

History

Preferred citation

(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_17

Journal title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11864 LNCS

Publication date

01/01/2019

Pagination

253-266

Publisher

Springer International Publishing

Publication status

Published

Online publication date

11/11/2019

ISSN

0302-9743

eISSN

1611-3349

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