Papers by Fatma Elsafoury
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models (2022.acl-srw)
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| Challenge: | a recent study shows that machine learning models are biased and they might make the wrong decisions for the wrong reasons. |
| Approach: | They investigate the impact of social bias on the performance of hate speech detection models . they also investigate the causal effect of intersectional bias on models' unfairness . |
| Outcome: | The proposed model is biased and makes the wrong decisions for the wrong reasons. |
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings (2022.coling-1)
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| Challenge: | Systematic Offensive Stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity. |
| Approach: | They propose a quantitative measure of the systematic offensive stereotyping (SOS) in word embeddings and validate it in most commonly used word embeds. |
| Outcome: | The proposed measure correlates with published statistics on online extremism, but does not explain hate speech detection models. |