This project addresss the evaluation of group fairness in commercial search engines that may lead to discrimination against certain
groups of individuals. More precisely, we measure fairness with respect to gender and nationality in a set of platforms where users can
search for second language teachers completely online. These websites
must rank available teachers for visitors, and disparate exposure may
lead to uneven economic benefit. We evaluate if teachers belonging
to protected groups are ...
This project addresss the evaluation of group fairness in commercial search engines that may lead to discrimination against certain
groups of individuals. More precisely, we measure fairness with respect to gender and nationality in a set of platforms where users can
search for second language teachers completely online. These websites
must rank available teachers for visitors, and disparate exposure may
lead to uneven economic benefit. We evaluate if teachers belonging
to protected groups are fairly represented in the first positions of the
rankings, and we conduct a statistical analysis of price depending on
nationality and gender across languages and platforms. Our results
put forward that although most lists are fair for all groups, there are
some worrisome exceptions. Finally, we found out that women and
people from high-income countries charge significantly higher fees for
their classes.
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