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This paper is a summary of the work done in my PhD thesis. Where Iinvestigate the impact of bias in NLP models on the task of hate speechdetection from three perspectives: explainability, offensive stereotyping bias,and fairness. Then, I discuss the main takeaways from my thesis and how theycan benefit the broader NLP community. Finally, I discuss important futureresearch directions. The findings of my thesis suggest that the bias in NLPmodels impacts the task of hate speech detection from all three perspectives.And that unless we start incorporating social sciences in studying bias in NLPmodels, we will not effectively overcome the current limitations of measuringand mitigating bias in NLP models.

Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection