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Fashion, a highly subjective topic is interpreted differently by allindividuals. E-commerce platforms, despite these diverse requirements, tend tocater to the average buyer instead of focusing on edge cases like non-binaryshoppers. This case study, through participant surveys, shows that visualsearch on e-commerce platforms like Amazon, Beagle.Vision and Lykdat, isparticularly poor for non-binary clothing items. Our comprehensive quantitativeanalysis shows that these platforms are more robust to binary clothing inputs.The non-binary clothing items are recommended in a haphazard manner, asobserved through negative correlation coefficients of the ranking order. Theparticipants also rate the non-binary recommendations lower than the binaryones. Another intriguing observation is that male raters are more inclined tomake binary judgements compared to female raters. Thus it is clear that thesesystems are not inclusive to the minority, disadvantaged communities ofsociety, like LGBTQ+ people. We conclude with a call to action for thee-commerce platforms to take cognizance of our results and be more inclusive.

Marching with the Pink Parade: Evaluating Visual Search Recommendations for Non-binary Clothing Items