Deep Concept Removal
This addresses fairness and robustness issues in AI by enabling models to avoid encoding sensitive attributes, though it is incremental as it builds on existing adversarial and probing techniques.
The paper tackles the problem of concept removal in deep neural networks to learn representations that exclude specified attributes like gender, proposing a method that uses adversarial linear classifiers to remove targeted concepts while maintaining performance and improving out-of-distribution generalization.
We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e.g., gender etc.) We propose a novel method based on adversarial linear classifiers trained on a concept dataset, which helps to remove the targeted attribute while maintaining model performance. Our approach Deep Concept Removal incorporates adversarial probing classifiers at various layers of the network, effectively addressing concept entanglement and improving out-of-distribution generalization. We also introduce an implicit gradient-based technique to tackle the challenges associated with adversarial training using linear classifiers. We evaluate the ability to remove a concept on a set of popular distributionally robust optimization (DRO) benchmarks with spurious correlations, as well as out-of-distribution (OOD) generalization tasks.