Abstract Machine Learning techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate recidivism prediction, consumer credit–risk analysis insurance pricing. The prevalence machine learning has raised concerns about the potential for learned algorithms to biased against certain groups. Many definitions been proposed literature, but fu...

Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents modeling procedure that carefully builds models around class information in order make sure final model is independent status, even nonlinear sense. works for any method. The was tested on subprime credit card data combined with demographic by zip code fr...

The vast advances in Machine Learning over the last ten years have been powered by availability of suitably prepared data for training purposes. future ML-enabled enterprise hinges on data. As such, there is already a vibrant market offering annotation services to tailor sophisticated ML models. In this paper, we present research practical problem obtaining order improve accuracy We consider an...

In the Design of Experiments, we seek to relate response variables explanatory factors. Response Surface methodology (RSM) approximates relation between output and a polynomial transform using linear model. Some researchers have tried adjust other types models, mainly nonlinear nonparametric. We present large panel Machine Learning approaches that may be good alternatives classical RSM approxim...