Automated Gender Bias Evaluation in YouTube

Students are increasingly using online materials to learn new subjects or to supplement their learning process in educational institutions. Issues regarding gender bias have been raised in the context of formal education and some measures have been proposed to mitigate them. In our previous work, we investigate the perceived gender bias in YouTube using manually annotations for detecting the narrators' perceived gender in educational videos. In this work, our goal is to evaluate the perceived gender bias in online education by exploiting an automated annotations. The automated pipeline has already proposed in a recent paper, thus in this paper we only share our empirical results with important findings. Our results show that educational videos are biased towards the male and STEM-related videos are more biased than their NON-STEM counterparts.