Search Engine Gender Bias

Abstract

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 via 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.

Representativeness and face-ism: Gender bias in image search

“…The automatization greatly simplifies operationalization of face-ism detection. This is important in the context of previous attempts to address biases, for example, attempts to alert users to biased search results ( Epstein et al, 2017 ), the provision of dialectic search that presents alternative views ( Wijnhoven and Brinkhuis, 2015 ; Wijnhoven and Van Haren, 2021 ), or the design of better training datasets for the underlying algorithms ( Buolamwini and Gebru, 2018 ). Our approach thus also yields an opportunity for debiasing, for example, by including the face-ism index in image selection processes.…”

“…There are several considerations when it comes to auditing search engine results: (1) personalization, that is, the adjustment of search results according to user characteristics ( Hannak et al, 2013 ) such as the location from which the searches are requested ( Kliman-Silver et al, 2015 ), the previous browsing history ( Haim et al, 2018 ; Mikians et al, 2012 ; Robertson et al, 2018 ) or the user profiles which can include the individual’s gender ( Wijnhoven and Van Haren, 2021 ); (2) randomization, that is, unexplained differences that emerge even under the seemingly equal browsing conditions (location, browser type, incognito mode); and (3) time effects, that is, the adaptation of results according to the historical context at play during the data collection ( Metaxa et al, 2019 ; Urman and Makhortykh, 2021 ).…”

Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search

“…By detecting gender labels of the photographs of U.S. members of Congress and their tweeted images, Schwemmer et al (2020) concluded Google Cloud Vision (GCV) could produce correct and biased labels at the same time because a subset of many possible true labels was selectively reported. Wijnhoven (2021) found a gender bias toward stereotypically female jobs for women but also for men when searching jobs via Google search engine. By examining four professions across digital platforms, Singh et al (2020) concluded: 1) gender stereotypes were most likely to be challenged when users acted directly to create and curate content, and 2) algorithmic approaches for content curation showed little inclination towards breaking stereotypes.…”

A canvas for the ethical design of learning experiences with digital tools

“…Distance/online teaching has also developed the use of video conferencing systems, online whiteboards or collaborative document edition tools. Because they generate digital traces of users [1,2] and can potentially be algorithmically biased [3][4][5] -among other issues, these tools must be used with special attention to ethical questions and this is often left to the responsibility of teachers.…”