Consistent Range Approximation for Fair Predictive Modeling
This addresses fairness certification for predictive models, particularly in scenarios with biased data, offering a novel approach but appearing incremental in its application of database query techniques.
The paper tackles the problem of certifying fairness of predictive models trained on biased data by proposing a consistent range approximation framework, which shows substantial improvement over existing state-of-the-art methods in evaluations on real data.
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.