Investigating User Perception of Gender Bias in Image Search: The Role of Sexism. Otterbacher, J., Checco, A., Demartini, G., & Clough, P. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, of SIGIR '18, pages 933–936. ACM. event-place: Ann Arbor, MI, USA![Investigating User Perception of Gender Bias in Image Search: The Role of Sexism [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.
@inproceedings{otterbacher_investigating_2018,
location = {New York, {NY}, {USA}},
title = {Investigating User Perception of Gender Bias in Image Search: The Role of Sexism},
isbn = {978-1-4503-5657-2},
url = {http://doi.acm.org/10.1145/3209978.3210094},
doi = {10.1145/3209978.3210094},
series = {{SIGIR} '18},
shorttitle = {Investigating User Perception of Gender Bias in Image Search},
abstract = {There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.},
pages = {933--936},
booktitle = {The 41st International {ACM} {SIGIR} Conference on Research \& Development in Information Retrieval},
publisher = {{ACM}},
author = {Otterbacher, Jahna and Checco, Alessandro and Demartini, Gianluca and Clough, Paul},
urldate = {2019-07-10},
date = {2018},
note = {event-place: Ann Arbor, {MI}, {USA}},
keywords = {gender stereotypes, search engine bias, user perceptions}
}
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