ExFairness.Mitigation.Reweighting (ExFairness v0.5.1)

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Sample reweighting for fairness-aware machine learning.

Reweighting is a pre-processing technique that assigns different weights to training samples to achieve fairness. Samples from underrepresented groups or combinations receive higher weights.

How It Works

For demographic parity, the weight for sample (a, y) is:

w(a, y) = P(Y = y) / P(A = a, Y = y)

This ensures that all group-label combinations have equal expected weight, which helps achieve demographic parity after reweighting.

For equalized odds, weights are computed to balance both positive and negative outcomes across groups.

Usage

Compute weights during data preparation, then pass them to your training algorithm's sample_weight parameter.

References

  • Kamiran, F., & Calders, T. (2012). "Data preprocessing techniques for classification without discrimination." KAIS.

Examples

iex> labels = Nx.tensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
iex> sensitive = Nx.tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
iex> weights = ExFairness.Mitigation.Reweighting.compute_weights(labels, sensitive)
iex> Nx.size(weights)
20

Summary

Functions

compute_weights(labels, sensitive_attr, opts \\ [])

Computes sample weights for fairness-aware training.

Functions

compute_weights(labels, sensitive_attr, opts \\ [])

@spec compute_weights(Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: Nx.Tensor.t()

Computes sample weights for fairness-aware training.

Parameters

  • labels - Binary labels tensor (0 or 1)
  • sensitive_attr - Binary sensitive attribute tensor (0 or 1)
  • opts - Options:
    • :target - Target fairness metric (:demographic_parity or :equalized_odds, default: :demographic_parity)
    • :min_per_group - Minimum samples per group for validation (default: 10)

Returns

A tensor of sample weights (same shape as labels). Weights are normalized to have mean 1.0.

Examples

iex> labels = Nx.tensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
iex> sensitive = Nx.tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
iex> weights = ExFairness.Mitigation.Reweighting.compute_weights(labels, sensitive)
iex> mean = Nx.mean(weights) |> Nx.to_number()
iex> Float.round(mean, 2)
1.0