An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in BHollywood (Student Abstract)

Authors

  • Kunal Khadilkar Carnegie Mellon University
  • Ashiqur R. KhudaBukhsh Carnegie Mellon University

Keywords:

Bias, Language Model, Natural Language Processing, Sexism, Bollywood

Abstract

Bollywood, aka the Mumbai film industry, is one of the biggest movie industries in the world with a current movie market share of worth 2.1 billion dollars and a target audience base of 1.2 billion people. While the entertainment impact in terms of lives that Bollywood can potentially touch is mammoth, no NLP study on social biases in Bollywood content exists. We thus seek to understand social biases in a developing country through the lens of popular movies. Our argument is simple -- popular movie content reflects social norms and beliefs in some form or shape. We present our preliminary findings on a longitudinal corpus of English subtitles of popular Bollywood movies focusing on (1) social bias toward a fair skin color (2) gender biases, and (3) gender representation. We contrast our findings with a similar corpus of Hollywood movies. Surprisingly, we observe that much of the biases we report in our preliminary experiments on the Bollywood corpus, also gets reflected in the Hollywood corpus.

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Published

2021-05-18

How to Cite

Khadilkar, K., & R. KhudaBukhsh, A. (2021). An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in BHollywood (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15813-15814. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17903

Issue

Section

AAAI Student Abstract and Poster Program