Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
This work addresses fairness issues in machine learning models for decision-making applications, offering an incremental improvement over existing methods.
The paper tackles the problem of mitigating biases in pre-trained classifiers to improve fairness metrics like demographic parity and equality of opportunity, by dropping selected training data points without refitting the model, resulting in substantial fairness improvements without significantly reducing predictive performance.
In consequential decision-making applications, mitigating unwanted biases in machine learning models that yield systematic disadvantage to members of groups delineated by sensitive attributes such as race and gender is one key intervention to strive for equity. Focusing on demographic parity and equality of opportunity, in this paper we propose an algorithm that improves the fairness of a pre-trained classifier by simply dropping carefully selected training data points. We select instances based on their influence on the fairness metric of interest, computed using an infinitesimal jackknife-based approach. The dropping of training points is done in principle, but in practice does not require the model to be refit. Crucially, we find that such an intervention does not substantially reduce the predictive performance of the model but drastically improves the fairness metric. Through careful experiments, we evaluate the effectiveness of the proposed approach on diverse tasks and find that it consistently improves upon existing alternatives.