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.

Addressing Social Bias in Information Retrieval

“…The ACM Statement's first principle is Awareness -that all stakeholders, from system designers and engineers, to the end users, should be aware of possible biases of the system as well as their potential harms. To this end, in [7], my colleagues and I explored users' awareness of gender bias in image search results sets. We hypothesized that users who are more sexist, would be less likely to indicate that a heavily gender-imbalanced set of images is "subjective" as compared to less sexist users.…”

“…Another recent development is the Association for Computing Machinery's Statement on Algorithmic Transparency and Accountability, which has been approved by both the ACM U.S. Public Policy Council and the Europe Policy Committee 7 . The statement notes that many algorithmic processes are opaque and that the reasons for this may vary.…”

“…https://ethicsinaction.ieee.org/. 6 https://standards.ieee.org/develop/project/7003.html 7. https://www.acm.org/binaries/content/assets/public-policy/2017 joint statement algorithms.pdf.…”

Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

“…My husband is a good man, but only seems to stay with me out of duty. ing and mitigating gender bias for three additional reasons: first, it has multiple potential sources of bias-not just dialogues, but also characters and personas-second, it was crowdsourced, and thus susceptible to reflecting the gender biases known to be present in crowdworkers' annotations (Otterbacher et al, 2018;Barbosa and Chen, 2019), and third, LIGHT's medieval, fantasy setting might encourage crowdworkers to impart text with their gender biases. After selecting LIGHT for particular scrutiny, we then explore three bias mitigation techniques, one of which is wholly novel, and another which is novel in its application to dialogue: (i) Counterfactual Data Augmentation (CDA) (Hall Maudslay et al, 2019;Zmigrod et al, 2019), (ii) a targeted data collection method, which we refer to as Positive-Bias Data collection, and (iii) Bias Controlled text generation.…”

Advances in Bias and Fairness in Information Retrieval

“…Currently, research on race and gender bias in image search focuses on visual representation of a few subjects, such as professional occupations [30] or emotions [41]. However, there is a growing recognition that representation of other aspects of contemporary societies can also be genderly or racially skewed.…”