Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 5, 25 May 2023


Open Access | Article

Bias in Search Engine: the Case of Google and a Workshop Solution

Ruixin Huang * 1 , Jiatian Li 2 , Qi Luo 3
1 School of Computer Science, the University of Malaya, Kuala Lumpur, 50603 (post), Malaysia
2 School of Arts, the Chinese University of Hong Kong, Central Ave, Hong Kong
3 School of Computer Science, the University of Birmingham, Edgbaston, Birmingham, B152TT (post), the UK

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 5, 156-162
Published 25 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ruixin Huang, Jiatian Li, Qi Luo. Bias in Search Engine: the Case of Google and a Workshop Solution. TNS (2023) Vol. 5: 156-162. DOI: 10.54254/2753-8818/5/20230347.

Abstract

The search engine (SE) is a senseless artificial program. SE matches the user's information demands with the input information and then provides an ordered list of answers. However, the outputs are frequently subjected to bias, which can affect the depiction of issues like gender inequality. Studies have shown that search engines may unconsciously inherit biases from their creators and users throughout their life cycle. In this paper, focused on Google as our research case, we evaluate and summarize different factors that can lead to the bias issue. The factors are depicted in computer science social domains. And in response to these causes, we propose a workshop idea to raise awareness of the problem of search engine discrimination, especially regarding gender issues. Based on our current workshop solution, we also list some potential improvements.

Keywords

The Search engine, Google, Bias, Gender bias, Workshop.

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
ISBN (Print)
978-1-915371-53-9
ISBN (Online)
978-1-915371-54-6
Published Date
25 May 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/5/20230347
Copyright
25 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated