26 citations · 53 across the 7 of their papers we have counts for
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cs.LG2021★ 26 cited
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Boris van Breugel, Trent Kyono, Jeroen Berrevoets +1
Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we foc…
cs.CL2021★ 13 cited
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models
Daniel de Vassimon Manela, David Errington, Thomas Fisher +2
This paper proposes two intuitive metrics, skew and stereotype, that quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun r…