Through a fair looking-glass: mitigating bias in image datasets
- URL: http://arxiv.org/abs/2209.08648v1
- Date: Sun, 18 Sep 2022 20:28:36 GMT
- Title: Through a fair looking-glass: mitigating bias in image datasets
- Authors: Amirarsalan Rajabi, Mehdi Yazdani-Jahromi, Ozlem Ozmen Garibay, Gita
Sukthankar
- Abstract summary: We present a fast and effective model to de-bias an image dataset through reconstruction and minimizing the statistical dependence between intended variables.
We evaluate our proposed model on CelebA dataset, compare the results with a state-of-the-art de-biasing method, and show that the model achieves a promising fairness-accuracy combination.
- Score: 1.0323063834827415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the recent growth in computer vision applications, the question of how
fair and unbiased they are has yet to be explored. There is abundant evidence
that the bias present in training data is reflected in the models, or even
amplified. Many previous methods for image dataset de-biasing, including models
based on augmenting datasets, are computationally expensive to implement. In
this study, we present a fast and effective model to de-bias an image dataset
through reconstruction and minimizing the statistical dependence between
intended variables. Our architecture includes a U-net to reconstruct images,
combined with a pre-trained classifier which penalizes the statistical
dependence between target attribute and the protected attribute. We evaluate
our proposed model on CelebA dataset, compare the results with a
state-of-the-art de-biasing method, and show that the model achieves a
promising fairness-accuracy combination.
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