how-machine-learning-introduces-unconscious-biases

Yet the inexperienced or rushed data scientist skipped past feature engineering, the critical stage at which those invalid fields would have been removed. The experienced data scientist would know to invest lots of time in feature engineering to explicitly screen out potential bias from our training data. If our hiring data to date has a past human bias of not hiring women at the same rate as men, our machine learning model would learn to emulate that behavior unless we explicitly removed gender from consideration. It's easy to see how bias could creep in if inexperienced or rushed data scientists are building models from massive datasets.