Nonlinear Concept Erasure: a Density Matching Approach
This addresses fairness concerns in real-world applications by preventing inference of sensitive attributes like gender or race from text, though it is incremental as it builds on existing concept erasure methods.
The paper tackles the problem of removing sensitive demographic information from neural text representations to ensure fairness, achieving state-of-the-art performance in nonlinear erasure of discrete attributes on NLP benchmarks and effectively mitigating bias in deep nonlinear classifiers.
Ensuring that neural models used in real-world applications cannot infer sensitive information, such as demographic attributes like gender or race, from text representations is a critical challenge when fairness is a concern. We address this issue through concept erasure, a process that removes information related to a specific concept from distributed representations while preserving as much of the remaining semantic information as possible. Our approach involves learning an orthogonal projection in the embedding space, designed to make the class-conditional feature distributions of the discrete concept to erase indistinguishable after projection. By adjusting the rank of the projector, we control the extent of information removal, while its orthogonality ensures strict preservation of the local structure of the embeddings. Our method, termed $\overline{\mathrm{L}}$EOPARD, achieves state-of-the-art performance in nonlinear erasure of a discrete attribute on classic natural language processing benchmarks. Furthermore, we demonstrate that $\overline{\mathrm{L}}$EOPARD effectively mitigates bias in deep nonlinear classifiers, thereby promoting fairness.