Lessons learned from analyzing systems for hate speech detection and bias mitigation

To classify as hate speech or not, that is the question!

Given the subjectivity in defining hate speech, building systems for hate speech detection via NLP has been a challenge. The subjective understanding of hate speech makes the analysis and mitigation of biases that usually exist in NLP systems even more challenging and open-ended. In this talk, the authors will share about their experience in curating a hate speech dataset in the Indic context and modeling it with contextual information. The authors will also shed light on uncovering the “knowledge drift” in KG-based bias mitigation in hate speech detection systems as a part of their study surveying and reproducing such systems.

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Sarah Masud is a 4th year Ph.D. student in the area of social computing based out of IIIT-Delhi. Her time is well-spent analyzing hateful content one post at a time.