  Fashion, a highly subjective topic is interpreted differently by all
individuals. E-commerce platforms, despite these diverse requirements, tend to
cater to the average buyer instead of focusing on edge cases like non-binary
shoppers. This case study, through participant surveys, shows that visual
search on e-commerce platforms like Amazon, Beagle.Vision and Lykdat, is
particularly poor for non-binary clothing items. Our comprehensive quantitative
analysis shows that these platforms are more robust to binary clothing inputs.
The non-binary clothing items are recommended in a haphazard manner, as
observed through negative correlation coefficients of the ranking order. The
participants also rate the non-binary recommendations lower than the binary
ones. Another intriguing observation is that male raters are more inclined to
make binary judgements compared to female raters. Thus it is clear that these
systems are not inclusive to the minority, disadvantaged communities of
society, like LGBTQ+ people. We conclude with a call to action for the
e-commerce platforms to take cognizance of our results and be more inclusive.
