Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning
This addresses fairness in machine learning for scenarios where protected attributes are unavailable, offering a practical tool for developers and regulators, though it is incremental as it builds on existing fairness regularization methods.
The paper tackles the problem of ensuring fairness in AI systems when protected attributes are not explicitly available, by introducing a model-agnostic approach based on example pairs judged by a fair domain expert to warrant similar model responses. It demonstrates the method on the Income Census dataset, achieving improved fairness metrics without specifying concrete numbers.
As AI systems develop in complexity it is becoming increasingly hard to ensure non-discrimination on the basis of protected attributes such as gender, age, and race. Many recent methods have been developed for dealing with this issue as long as the protected attribute is explicitly available for the algorithm. We address the setting where this is not the case (with either no explicit protected attribute, or a large set of them). Instead, we assume the existence of a fair domain expert capable of generating an extension to the labeled dataset - a small set of example pairs, each having a different value on a subset of protected variables, but judged to warrant a similar model response. We define a performance metric - paired consistency. Paired consistency measures how close the output (assigned by a classifier or a regressor) is on these carefully selected pairs of examples for which fairness dictates identical decisions. In some cases consistency can be embedded within the loss function during optimization and serve as a fairness regularizer, and in others it is a tool for fair model selection. We demonstrate our method using the well studied Income Census dataset.