A review of deep learning fairness that maps bias sources and mitigation methods across data, training, and inference stages, with a focus on interpretability-driven detection.
Bal- anced datasets are not enough: Estimating and mitigating gender bias in deep image representations,
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Fairness in Deep Learning: A Computational Perspective
A review of deep learning fairness that maps bias sources and mitigation methods across data, training, and inference stages, with a focus on interpretability-driven detection.