Detecting race and gender bias in visual representation of AI on web search engines

Makhortykh, Mykola; Urman, Aleksandra; Ulloa, Roberto (1 April 2021). Detecting race and gender bias in visual representation of AI on web search engines (Unpublished). In: Second International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2021). Lucca, Tuscany. April 1, 2021 09:00-16:30 - ONLINE EVENT.

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Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB)
UniBE Contributor:	Makhortykh, Mykola, Urman, Aleksandra
Subjects:	000 Computer science, knowledge & systems 300 Social sciences, sociology & anthropology 600 Technology
Language:	English
Submitter:	Mykola Makhortykh
Date Deposited:	05 May 2021 16:18
Last Modified:	05 Dec 2022 15:50
Additional Information:	Part of the 43rd European Conference on Information Retrieval (ECIR 2021)
Uncontrolled Keywords:	bias, search engine, information retrieval, race, gender, artificial intelligence, google
URI:	https://boris.unibe.ch/id/eprint/155725