Parametric Fairness with Statistical Guarantees

Broadly speaking, approaches to obtain algorithmic Algorithmic fairness has gained prominence due to societal fairness can be categorized into pre-processing methods and regulatory concerns about biases in Machine which enforce fairness in the data before applying machine Learning models. Common group fairness metrics like learning models Calmon et al. (2017); Adebayo Equalized Odds for classification or Demographic Parity and Kagal (2016), in-processing methods, who achieve for both classification and regression are widely used and fairness in the training step of the learning model Agarwal a host of computationally advantageous post-processing et al. (2018); Donini et al. (2018); Agarwal et al. methods have been developed around them. However, (2019), and post-processing which reduces unfairness in these metrics often limit users from incorporating domain the model inferences following the learning procedure knowledge. Despite meeting traditional fairness Chiappa et al. (2020); Chzhen et al. (2020c,a); Denis criteria, they can obscure issues related to intersectional et al. (2021). Our work falls into the latter, as this fairness and even replicate unwanted intra-group biases category of algorithms offers computational advantages in the resulting fair solution. To avoid this narrow and are easiest to integrate in existing machine learning perspective, we extend the concept of Demographic pipelines. Parity to incorporate distributional properties in the Most of the current studies involving post-processing predictions, allowing expert knowledge to be used in the methods employ a neutral approach to enforcing DPfairness, fair solution. We illustrate the use of this new metric where model outputs are taken as given and through a practical example of wages, and develop a fairness is achieved by constructing a common distribution.