Urman, Aleksandra; Makhortykh, Mykola; Ulloa, Roberto (2022). Auditing the representation of migrants in image web search results. Humanities and Social Sciences Communications, 9(1), p. 130. Springer Nature 10.1057/s41599-022-01144-1
|
Text
s41599-022-01144-1.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (2MB) | Preview |
Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB) |
UniBE Contributor: |
Urman, Aleksandra, Makhortykh, Mykola |
Subjects: |
000 Computer science, knowledge & systems 300 Social sciences, sociology & anthropology 300 Social sciences, sociology & anthropology > 360 Social problems & social services |
ISSN: |
2662-9992 |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Mykola Makhortykh |
Date Deposited: |
20 Jun 2022 15:49 |
Last Modified: |
05 Dec 2022 16:20 |
Publisher DOI: |
10.1057/s41599-022-01144-1 |
Uncontrolled Keywords: |
migrant, bias, refugees, search engine, algorithm audit, algorithms |
BORIS DOI: |
10.48350/170645 |
URI: |
https://boris.unibe.ch/id/eprint/170645 |
Actions (login required)
 |
Edit item |