Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies


Nishtha Madaan, Sameep Mehta, Taneea Agrawaal, Vrinda Malhotra, Aditi Aggarwal, Yatin Gupta, Mayank Saxena ;
Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:92-105, 2018.


The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying such stereotypes and bias in Hindi movie industry (\it Bollywood) and propose an algorithm to remove these stereotypes from text. We analyze movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at sentence and intra-sentence level. Different features like occupation, introductions, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereotype in movies. Using the derived semantic graph, we compute centrality of each character and observe similar bias there. We also show that such bias is not applicable for movie posters where females get equal importance even though their character has little or no impact on the movie plot. The silver lining is that our system was able to identify 30 movies over last 3 years where such stereotypes were broken. The next step, is to generate debiased stories. The proposed debiasing algorithm extracts gender biased graphs from unstructured piece of text in stories from movies and de-bias these graphs to generate plausible unbiased stories.

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