Search Results for author:
Found 7 papers, 1 papers with code
A Comparative Study on Word Embeddings and Social NLP Tasks
In recent years, gray social media platforms, those with a loose moderation policy on cyberbullying, have been attracting more users.
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models
Finally, I investigate the causal effect of the social and intersectional bias on the performance and unfairness of hate speech detection models.
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms.
Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection
This paper is a summary of the work done in my PhD thesis.
Systematic Offensive Stereotyping (SOS) Bias in Language Models
Finally, we investigate the impact of the SOS bias in LMs on their performance and fairness on hate speech detection.
On Bias and Fairness in NLP: Investigating the Impact of Bias and Debiasing in Language Models on the Fairness of Toxicity Detection
Even though there is evidence that language models are biased, the impact of that bias on the fairness of downstream NLP tasks is still understudied.
On the Origins of Bias in NLP through the Lens of the Jim Code
In this paper, we trace the biases in current natural language processing (NLP) models back to their origins in racism, sexism, and homophobia over the last 500 years.
