Fair Interpretable Representation Learning with Correction Vectors
This addresses the need for interpretable fairness methods in AI, particularly for compliance with regulations like those in the EU, though it is incremental as it builds on existing debiasing techniques.
The paper tackles the problem of making fair representation learning more interpretable by introducing a framework based on 'correction vectors' that maintain data dimensionality, and shows that this approach achieves state-of-the-art results without performance loss in ranking or classification tasks.
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various representation debiasing techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning that is centered around the learning of "correction vectors", which have the same dimensionality as the given data vectors. Correction vectors may be computed either explicitly via architectural constraints or implicitly by training an invertible model based on Normalizing Flows. We show experimentally that several fair representation learning models constrained in such a way do not exhibit losses in ranking or classification performance. Furthermore, we demonstrate that state-of-the-art results can be achieved by the invertible model. Finally, we discuss the law standing of our methodology in light of recent legislation in the European Union.