Group Fairness with Uncertainty in Sensitive Attributes
This addresses fairness in high-stakes applications where sensitive data is often noisy or missing, offering a robust solution for practitioners, though it is incremental as it builds on existing fairness optimization methods.
The paper tackles the problem of learning fair predictive models when sensitive attributes are uncertain, showing that standard fairness constraints under uncertainty can fail to achieve desired fairness levels. It proposes a bootstrap-based algorithm that ensures target fairness guarantees despite uncertainty, validated on real-world classification and regression tasks.
Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solving an optimization problem that maximizes the predictive power of the model under an appropriate group fairness constraint. However, in practice, sensitive attributes are often missing or noisy resulting in uncertainty. We demonstrate that solely enforcing fairness constraints on uncertain sensitive attributes can fall significantly short in achieving the level of fairness of models trained without uncertainty. To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes. The algorithm is guided by a Gaussian analysis for the independence notion of fairness where we propose a robust quadratically constrained quadratic problem to ensure a strict fairness guarantee with uncertain sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks for various group fairness notions, e.g., independence and separation.