OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning

Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or females) according to various fairness metrics. Existing techniques for producing fair ML models either are limited to the type of fairness constraints they can handle (e.g., preprocessing) or require nontrivial modifications to downstream ML training algorithms (e.g., in-processing). We propose a declarative system Omni Fair for supporting group fairness in ML. Omni Fair features a declarative interface for users to specify desired group fairness constraints and supports all commonly used group fairness notions, including statistical parity, equalized odds, and predictive parity. Omni Fair is also model-agnostic in the sense that it does not require modifications to a chosen ML algorithm. Omni Fair also supports enforcing multiple user declared fairness constraints simultaneously while most previous techniques cannot. The algorithms in Omni Fair maximize model accuracy while meeting the specified fairness constraints, and their efficiency is optimized based on the theoretically provable monotonicity property regarding the trade-off between accuracy and fairness that is unique to our system. We conduct experiments on commonly used datasets that exhibit bias against minority groups in the fairness literature. We show that OmniFair is more versatile than existing algorithmic fairness approaches in terms of both supported fairness constraints and downstream ML models. OmniFair reduces the accuracy loss by up to 94.8% compared with the second best method. Omni Fair also achieves similar running time to preprocessing methods, and is up to 270x faster than in-processing methods.