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.
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.
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.
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
Our results suggest that all the inspected LMs are SOS biased.
On Bias and Fairness in NLP: How to have a fairer text classification?
In this paper, we provide a holistic analysis of the different sources of bias, Upstream, Sample and Overampflication biases, in NLP models.
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.
