Provide a simple example where bias might skew results in a machine learning model.

Sure, let's consider a machine learning model that is designed to predict whether a person will default on a loan. The model is trained on a dataset that includes a variety of features such as income, employment status, credit score, and age.

Step 1: Data Collection The bank collects data from its customers over several years. This data includes information about the customers' income, employment status, credit score, and whether they defaulted on their loans.

Step 2: Data Preprocessing The bank preprocesses the data to handle missing values, outliers, and irrelevant features. The data is then split into a training set and a test set.

Step 3: Model Training The bank uses the training set to train a machine learning model. The model learns to predict whether a customer will default on a loan based on their income, employment status, and credit score.

Step 4: Model Evaluation The bank evaluates the model's performance on the test set. The model appears to perform well, with a high accuracy rate.

However, there is a potential bias in this model. Suppose the bank has historically not given loans to younger people, so there are very few young people in the dataset. As a result, the model may not perform well when it encounters data from a young person because it has not learned from enough examples of young people. This is an example of selection bias, where the data used to train the model is not representative of the population the model will be applied to. This bias can skew the results of the model, leading to inaccurate predictions.