Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.
Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity.
We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously.
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.
As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups.
Social and Information Networks Computers and Society
Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article.
Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.