Fair Credit Scorer through Bayesian Approach
This addresses fairness in credit scoring, an important real-world application, though it appears incremental as it applies existing Bayesian methods to a known fairness problem.
The paper tackles unfair credit scoring against protected groups by constructing a fair prediction model that removes correlation between protected attributes and observable features using latent variables. They implement this with Bayesian approaches including MCMC simulation.
Machine learning currently plays an increasingly important role in people's lives in areas such as credit scoring, auto-driving, disease diagnosing, and insurance quoting. However, in many of these areas, machine learning models have performed unfair behaviors against some sub-populations, such as some particular groups of race, sex, and age. These unfair behaviors can be on account of the pre-existing bias in the training dataset due to historical and social factors. In this paper, we focus on a real-world application of credit scoring and construct a fair prediction model by introducing latent variables to remove the correlation between protected attributes, such as sex and age, with the observable feature inputs, including house and job. For detailed implementation, we apply Bayesian approaches, including the Markov Chain Monte Carlo simulation, to estimate our proposed fair model.