A machine learning model makes predictions of an outcome for a particular instance. (Given an instance of a loan application, predict if the applicant will repay the loan.) The model makes these predictions based on a training dataset, where many other instances (other loan applications) and actual outcomes (whether they repaid) are provided. Thus, a machine learning algorithm will attempt to find patterns, or generalizations, in the training dataset to use when a prediction for a new instance is needed. (For example, one pattern it might discover is "if a person has salary > USD 40K and has outstanding debt < USD 5, they will repay the loan".) In many domains this technique, called supervised machine learning, has worked very well. However, sometimes the patterns that are found may not be desirable or may even be illegal. For example, a loan repay model may determine that age plays a significant role in the prediction of repayment because the training dataset happened to have better repayment for one age group than for another. This raises two problems: 1) the training dataset may not be representative of the true population of people of all age groups, and 2) even if it is representative, it is illegal to base any decision on a applicant's age, regardless of whether this is a good prediction based on historical data.