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Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes.
The Berkeley FrameNet project
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Tagging performance correlates with author age
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Demographic Dialectal Variation in Social Media: A Case Study of African-American English
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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
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Adversarial Removal of Demographic Attributes from Text Data
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Mitigating unwanted biases with adversarial learning
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Adversarial Removal of Demographic Attributes Revisited
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Data-efficient image recognition with contrastive predictive coding
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Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Huang, X.; Xing, L.; Dernoncourt, F.; and Paul, M. J. 2020 · 2020
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Supervised Contrastive Learning
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Bias in Bios: A Case Study of semantic Representation Bias in a High-stakes Setting
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A simple framework for contrastive learning of visual representations
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Big Self-Supervised Models are Strong Semi-Supervised Learners
Chen, T.; Kornblith, S.; Swersky, K.; Norouzi, M.; and Hinton, G. E. 2020b
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Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
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Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
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