This repository contains an implementation of Relaxed Linear Adversarial Concept Erasure (RLACE). Given a dataset X of dense representations and labels y for some concept (e.g. gender), the method identifies a rank-k subsapce whose neutralization (suing an othogonal projection matrix) prevents linear classifiers from recovering the concept from the representations.

The method relies on a relaxed and constrained version of a minimax game between a predictor that aims to predict y and a projection matrix P that is optimized to prevent the prediction.

How to run

A simple running example is provided within


The main method, solve_adv_game, receives several arguments, among them:

  • rank: the rank of the neutralized subspace. rank=1 is emperically enough to prevent linear prediction in binary classification problem.

  • epsilon: stopping criterion for the adversarial game. Stops if abs(acc – majority_acc) < epsilon.

  • optimizer_class: torch.optim optimizer

  • optimizer_params_predictor / optimizer_params_P: parameters for the optimziers of the predictor and the projection matrix, respectively.

Running example:

num_iters = 50000
optimizer_class = torch.optim.SGD
optimizer_params_P = {"lr": 0.003, "weight_decay": 1e-4}
optimizer_params_predictor = {"lr": 0.003,"weight_decay": 1e-4}
epsilon = 0.001 # stop 0.1% from majority acc
batch_size = 256

output = solve_adv_game(X_train, y_train, X_dev, y_dev, rank=rank, device="cpu", out_iters=num_iters, optimizer_class=optimizer_class, optimizer_params_P =optimizer_params_P, optimizer_params_predictor=optimizer_params_predictor, epsilon=epsilon,batch_size=batch_size)

Optimization: Even though we run a concave-convex minimax game, which is generallly “well-behaved”, optimziation with alternate SGD is still not completely straightforward, and may require some tuning of the optimizers. Accuracy is also not expected to monotonously decrease in optimization; we return the projection matrix which performed best along the entire game. In all experiments on binary classification problems, we identified a projection matrix that neutralizes a rank-1 subspace and decreases classification accuracy to near-random (50%).

Using the projection:

output that is returned from solve_adv_game is a dictionary, that contains the following keys:

  1. score: final accuracy of the predictor on the projected data.

  2. P_before_svd: the final approximate projection matrix, before SVD that guarantees it’s a proper orthogonal projection matrix.

  3. P: a proper orthogonal matrix that neutralizes a rank-k subspace.

The “clean” vectors are given by["P"]).