• Computer Science
  • Published 2018

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

@inproceedings{Wang2018BalancedDA,
  title={Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations},
  author={Tianlu Wang and Jieyu Zhao and Mark Yatskar and Kai-Wei Chang and Vicente Ordonez},
  year={2018}
}
In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much… CONTINUE READING

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