Automated Search Bias Models & YouTube Gender Bias Analysis

This work first presents our attempts to establish an automated model using state-of-the-art approaches for analysing bias in search results of Bing and Google. Secondly, in this paper we also aim to analyse YouTube video search results in terms of perceived gender bias, i.e. narrator's gender from the viewer's perspective. Experimental results indicate that the current class-wise F1-scores of our best model are not sufficient to establish an automated model for bias analysis. Thus, to evaluate YouTube video search results in terms of perceived gender bias, we use manual annotations.