Papers with bias evaluation metrics

2 papers
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (P19-2)

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Challenge: Existing methods to reduce gender bias in natural language datasets are inadequate.
Approach: They propose a loss function modification approach which equalizes the probabilities of male and female words in the output.
Outcome: The proposed approach outperforms existing methods in several aspects, especially in reducing gender bias in occupation words.
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020.emnlp-main)

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Challenge: a recent study has shown that data collection is neglected by ignoring the quality of data.
Approach: They propose to use latent semantics to evaluate selection bias in hate speech . they compare latent Dirichlet Allocation (LDA) to eleven hate speech corpora .
Outcome: The proposed method could be revisable before focusing on classification performance.

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