Editor's Note: At ODSC Europe 2019, Sray Agarwal will conduct a workshop on fairness and accountability demonstrating how to detect bias and remove bias from ML models. The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc are considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science community has been ignoring so far? The answer is yes--in the pursuit of accuracy, most models sacrifice "fairness" and "interpretability."

Editor's note: Andras is a speaker for ODSC West 2019! Datasets are almost never complete and this can introduce various biases to your analysis. Due to these biases, your supervised machine learning model can produce incorrect predictions. The goal of this post is to give you an idea of why some of the most common approaches for dealing with missing values often introduce some type of bias. At ODSC West 2019, I will describe the methods and techniques that can help you to arrive at an unbiased conclusion in the face of missing data.