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name: adversarial description: "Use Fairlearn adversarial fairness estimators with PyTorch or TensorFlow backends, callbacks, CUDA, and backend-specific troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Fairlearn adversarial

Use this sub-skill when the task asks for neural adversarial unfairness mitigation: AdversarialFairnessClassifier, AdversarialFairnessRegressor, PyTorch, TensorFlow, neural predictor/adversary models, callbacks, dynamic alpha, warm_start, CUDA, or backend import failures.

Quick workflow

1. Confirm the selected backend is installed. PyTorch CPU and optional CUDA were verified for this skill; TensorFlow is documented but unverified here.
2. Prepare numeric 2D float features X, labels y, and row-aligned sensitive_features.
3. Define predictor and adversary models with compatible backend classes, or use Fairlearn's list model builder for simple experiments.
4. Construct the estimator with backend, model, optimizer, constraint, learning-rate, epoch, batch, callback, and CUDA settings.
5. Fit with sensitive_features=....
6. Evaluate with ../assessment/, watching for mode collapse, accuracy loss, or unchanged disparity.

Read these references

• references/backends-and-training.md for constructor fields, backend selection, model shapes, data preprocessing, callbacks, CUDA, and evaluation.
• references/troubleshooting.md for missing backends, 2D input errors, invalid activations, BCE range failures, callbacks, and CUDA errors.
• scripts/smoke_torch_adversarial.py for a tiny explicit-PyTorch-model smoke check with optional CUDA.

Core APIs to recognize

• AdversarialFairnessClassifier(*, backend="auto", predictor_model=None, adversary_model=None, predictor_optimizer="Adam", adversary_optimizer="Adam", constraints="demographic_parity", learning_rate=0.001, alpha=1.0, epochs=1, batch_size=32, shuffle=False, progress_updates=None, skip_validation=False, callbacks=None, cuda=None, warm_start=False, random_state=None)
• AdversarialFairnessRegressor(...) with the same constructor surface.
• Supported constraint strings in this family: demographic_parity and equalized_odds.

Boundary rules

• This sub-skill owns neural adversarial mitigation only. Use ../reductions/ for sklearn-estimator constrained retraining and ../postprocessing/ for threshold adjustment.
• Use ../installation/ if backend installation is the main task.
• Use ../datasets/ for Adult/ACS data loading and ../assessment/ for final metric tables.
• Do not install TensorFlow or large GPU stacks unless the user explicitly chooses that backend.

Operating rules

• X must be two-dimensional and numeric. One-hot encode categoricals and scale numeric columns before neural training.
• Labels and sensitive features are auto-preprocessed as binary, categorical, or continuous, but row alignment is still the caller's responsibility.
• Do not mix backends: PyTorch models/optimizers go with the PyTorch backend; Keras/TensorFlow models go with TensorFlow.
• In the inspected PyTorch engine, binary losses use BCELoss; custom binary predictor/adversary modules should output values in [0, 1], usually with a final torch.nn.Sigmoid().
• Adversarial training is sensitive to hyperparameters. Start tiny, validate often, and use callbacks for early stopping or learning-rate/alpha schedules.
• Use cuda="cuda:0" only after torch.cuda.is_available() is true; otherwise run CPU.

Fast validation

CPU PyTorch smoke:

python sub-skills/adversarial/scripts/smoke_torch_adversarial.py

Optional CUDA smoke:

python sub-skills/adversarial/scripts/smoke_torch_adversarial.py --cuda cuda:0