Papers by Maria Jose Zambrano

1 papers
Unpacking Bias: An Empirical Study of Bias Measurement Metrics, Mitigation Algorithms, and Their Interactions (2024.lrec-main)

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Challenge: Word embeddings (WE) models reflect gender, racial, and religious stereotypes from the corpus on which they are trained.
Approach: They propose a method that carefully controls for word sets and vector normalization to address these factors.
Outcome: The proposed method detects consistency between different mitigation methods and the evaluation words used by the mitigation methods.

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