- David Bamman, Chris Dyer, Noah A. Smith. 2014, Steven Bird, Ewan Klein, E. Loper, Nat-527, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. 2019, Bert, Samuel Gehman, Suchin Gururangan, Maarten Sap, Dan Hendrycks, Kevin Gimpel. 2020, Gaussian, Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Feng Liao, M. Ravanelli, Y. Bengio, Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Y. Lecun, B. Boser, J. Denker, Don-608 nie Henderson, R. Howard, Wayne Hubbard, Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis. 2022. Biasly: a machine learning based platform for automatic racial discrimination detection in online texts. Abstract: Warning : this paper contains content that may 001 be offensive or upsetting. 002 Detecting hateful, toxic, and otherwise racist 003 or sexist language in user-generated online con-004 tents has become an increasingly important task 005 in recent years. Indeed, the anonymity, the 006 transience, the size of messages, and the dif-007 ficulty of management, facilitate the diffusion 008 of racist or hateful messages across the Inter-009 net. The critical influence of this cyber-racism 010 is no longer limited to social media, but also 011 has a significant effect on our society : corpo-012 rate business operation, users’ health, crimes, 013 etc. Traditional racist speech reporting chan-014 nels have proven inadequate due to the enor-015 mous explosion of information, so there is an 016 urgent need for a method to automatically and 017 promptly detect texts with racial discrimination. 018 We propose in this work, a machine learning-019 based approach to enable automatic detection 020 of racist text content over the internet. State-of-021 the-art machine learning models that are able 022 to grasp language structures are adapted in this 023 study. Our main contribution include 1) a large 024 scale racial discrimination data set collected 025 from three distinct sources and annotated ac-026 cording to a guideline developed by specialists, 027 2) a set of machine learning models with vari-028 ous architectures for racial discrimination de-029 tection, and 3) a web-browser-based software 030 that assist users to debias their texts when us-031 ing the internet. All these resources are made 032 publicly available.
