Are Chess Discussions Racist? An Adversarial Hate Speech Data Set (Student Abstract)

Authors

  • Rupak Sarkar Maulana Abul Kalam Azad University of Technology
  • Ashiqur R. KhudaBukhsh Carnegie Mellon University

Keywords:

Hate Speech, Adversarial Data Set, Racism

Abstract

On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radic's YouTube handle got blocked because it contained ``harmful and dangerous'' content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radic speculated that given the current political situation, a referral to ``black against white'', albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask the following research question: \emph{how robust are off-the-shelf hate-speech classifiers to out-of-domain adversarial examples?} We release a data set of 1,000 annotated comments where existing hate speech classifiers misclassified benign chess discussions as hate speech. We conclude with an intriguing analogy result on racial bias with our findings pointing out to the broader challenge of color polysemy.

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Published

2021-05-18

How to Cite

Sarkar, R., & KhudaBukhsh, A. R. (2021). Are Chess Discussions Racist? An Adversarial Hate Speech Data Set (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15881-15882. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17937

Issue

Section

AAAI Student Abstract and Poster Program