There is both subjective and objective approaches to removing bias ( de-baising techniques ) from the training datasets. It is observed that the sources of bias generally arise from the following data quality issues.
- Over and under-sampling
- Skewed samples
- Feature choice/limited features
- Proxies/redundant encodings
- Biases and injustice in the primary data source
The essential steps for de-biasing require identifying the subspace of bias within the dataset. Then the key task is to neutralise and soften the bias if not completely equalising them.
There are interesting approaches for de-biasing image datasets. Deb-face is trying to address the problem in automated face recognition algorithms by constructing a de-biasing adversarial neural network that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation. Adversarial learning is adopted to minimise the correlation among feature factors so as to reduce the bias influence. Please refer to the approach paper in the following site.
DebFace - Debiasing Face Recognition