Gender Bias.

By Bruce J Hillman. 2018

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Marta Ruiz Costa-jussà
2. Daniel de Vassimon Manela
3. Boris van Breugel
4. Thomas Fisher
5. D. Errington
6. David Errington
7. Pasquale Minervini
8. Joel Escudé Font

Citations in the corpus

• Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021, methodology): “Indeed, they mostly focus on measuring the assignment of stereotypical pronouns to professions (Zhao et al., 2018a).”
• Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques (2019, methodology): “The word embeddings are trained from the same corpus, using GloVe (Pennington et al., 2014) and GN-GloVe (Zhao et al., 2018b).”
• Mixed Signals: The Unintended Effects of Diversity Initiatives (2019, background): “In an important demonstration of effective diversity intervention, Devine et al.’s prejudice habit-breaking model (Carnes et al., 2015; Devine et al., 2012, 2017; Forscher, Mitamura, Dix, Cox, & Devine, 2017) capitalizes on participants’ internal motivation to treat everyone fairly.”