Investigating User Perception of Gender Bias in Image Search: The Role of Sexism

By Jahna Otterbacher, Alessandro Checco, Gianluca Demartini, Paul D. Clough. 2018

57 citations recorded by the source corpus.

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.

Subfields: Safety, Robustness & Interpretability

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “aired, up and tasked with generating a dialogue between the characters. All dialogues contained within LIGHT are entirely crowdsourced—thus susceptible to reﬂecting the gender biases of crowdworkers (Otterbacher et al., 2018;Barbosa and Chen,2019). We investigate characters, personas, and dialogues ”