Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
This work addresses fairness in machine learning for continuous attributes, offering a model-agnostic method that is incremental but extends applicability to kernel-based approaches.
The paper tackles the scarcity of fairness methods for continuous protected attributes in regression by generalizing iterative null-space projection to kernel methods, demonstrating competitive or improved performance with Support Vector Regression across multiple datasets.
With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development. Fairness notions commonly rely on protected attributes to assess potential biases. Here, the majority of literature focuses on discrete setups regarding both target and protected attributes. The literature on continuous attributes especially in conjunction with regression -- we refer to this as \emph{continuous fairness} -- is scarce. A common strategy is iterative null-space projection which as of now has only been explored for linear models or embeddings such as obtained by a non-linear encoder. We improve on this by generalizing to kernel methods, significantly extending the scope. This yields a model and fairness-score agnostic method for kernel embeddings applicable to continuous protected attributes. We demonstrate that our novel approach in conjunction with Support Vector Regression (SVR) provides competitive or improved performance across multiple datasets in comparisons to other contemporary methods.