#  Introduction   Whether they are well-established classifiers or state-of-the-art massive models like large language models (LLMs), building machine learning solutions often entails a risk: algorithms might silently adopt prejudices inherent in the historical training dataset they were trained on. But in a high-stakes scenario or one where data is sensitive, how can we audit whether a model is biased without compromising real-world information? This hands-on article guides you in training a simple classification model for "loan approval" on biased data. Based on this, we will use Mimesis , an open-source library that can help generate a perfectly balanced, counterfactual dataset. You'll be able to test "fake" users with identical financial backgrounds but different demographic characteristics, thereby determining whether the model discriminates against certain groups or not.…