Search Engine Gender Bias

• Published in: Frontiers in big data Vol. 4; p. 622106
• Main Authors: Wijnhoven, Fons, van Haren, Jeanna
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 26.05.2021
• Subjects: Big Data; DuckDuckGo; filter bubble; gender bias; Google; job search; personalization; political participation search; Google; gender bias; personalization; job search; DuckDuckGo; filter bubble
• ISSN: 2624-909X; 2624-909X
• DOI: 10.3389/fdata.2021.622106

This article discusses possible search engine page rank biases as a consequence of search engine profile information. After describing search engine biases, their causes, and their ethical implications, we present data about the Google search engine (GSE) and DuckDuckGo (DDG) for which only the first uses profile data for the production of page ranks. We analyze 408 search engine screen prints of 102 volunteers (53 male and 49 female) on queries for job search and political participation. For job searches GSE, we find a bias toward stereotypically "female" jobs for women but also for men, although the bias is significantly stronger for women. For political participation, the bias of GSE is toward more powerful positions. Contrary to our hypothesis, this bias is even stronger for women than for men. Our analysis of DDG does not give statistically significant page rank differences for male and female users. We, therefore, conclude that GSE's personal profiling is not reinforcing a gender stereotype. Although no gender differences in page ranks was found for DDG, DDG usage in general gave a bias toward "male-dominant" vacancies for both men and women. We, therefore, believe that search engine page ranks are not biased by profile ranking algorithms, but that page rank biases may be caused by many other factors in the search engine's value chain. We propose ten search engine bias factors with virtue ethical implications for further research.