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1
Detecting Gender Bias using Explainability ...
Abstract: Explanations for AI systems have been used to improve the trustworthiness of these systems. These explanations can be used to find the undesirable implicit biases that machine learning models can rely on for their outputs. We apply this concept to detect gender bias in sentiment analysis models for textual data. With the help of an Equity Evaluation Corpus (EEC), we used different gender signals for otherwise identical input to the system and use explanations from LIME and SHAP to find a trend of bias, and identify terms that contribute the most to it. ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
URL: https://dx.doi.org/10.48448/xtya-7b69
https://underline.io/lecture/39696-detecting-gender-bias-using-explainability
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2
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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3
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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4
Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
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5
Improving Multimodal fusion via Mutual Dependency Maximisation ...
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6
Does Commonsense help in detecting Sarcasm? ...
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7
LMSOC: An Approach for Socially Sensitive Pretraining ...
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8
Comparative Opinion Quintuple Extraction from Product Reviews ...
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9
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction ...
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10
Dimensional Emotion Detection from Categorical Emotion ...
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11
Adverse Drug Reaction Classification of Tweets with Fusion of Text and Drug Representations ...
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12
Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training ...
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13
Monolingual Pre-Trained Language Models for Tigrinya ...
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