#fair-supervised-learning
#fair-supervised-learning

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How to Reduce Majority Bias in AI Models | HackerNoon

This work explores the inductive biases of fair learning algorithms and proposes a robust optimization scheme to enhance demographic parity.

fromHackernoon

2 months ago

Achieving Fair AI Without Sacrificing Accuracy | HackerNoon

In our experiments, we applied the SA-DRO algorithm to the DDP-based KDE fair learning algorithm proposed by [11], and RFI proposed by [13]. We kept the fairness regularization penalty coefficient to be Î» = 0.9. The DRO regularization coefficient can take over the range [0, 1], in this table, we set Ïµ = 0.9 for SA-DRO case.

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#fair-supervised-learning#fair-supervised-learning

How to Reduce Majority Bias in AI Models | HackerNoon

Achieving Fair AI Without Sacrificing Accuracy | HackerNoon

#fair-supervised-learning
#fair-supervised-learning