How can you ensure your machine learning models predict risk for the right population?

1 Define your target population

Before you start building your machine learning models, you need to have a clear idea of who your target population is and what risk factors are relevant for them. For example, if you are predicting credit risk, you need to know the characteristics and behaviors of your potential borrowers, such as income, debt, credit history, and payment patterns. You also need to define the outcome variable that you want to predict, such as default, delinquency, or loss. By defining your target population and outcome variable, you can narrow down the scope of your problem and focus on the most important features and metrics.

2 Assess your data quality and representativeness

Once you have defined your target population, you need to collect and prepare the data that you will use to train and test your machine learning models. The quality and representativeness of your data are crucial for the validity and generalizability of your models. You need to ensure that your data is accurate, complete, consistent, and relevant for your target population and outcome variable. You also need to check that your data is representative of the diversity and distribution of your target population and that it does not contain any biases or outliers that could skew your results.

3 Evaluate your model performance and fairness

After you have built your machine learning models, you need to evaluate how well they perform and how fair they are for your target population. You need to use appropriate metrics and methods to measure the accuracy, precision, recall, and ROC curve of your models and compare them with baseline or alternative models. You also need to test your models on new or unseen data and assess how they generalize to different subgroups or segments of your target population. Moreover, you need to monitor and mitigate any potential sources of unfairness or discrimination in your models, such as algorithmic bias, data imbalance, or feature selection. You can use techniques such as fairness metrics, explainable AI, or adversarial learning to ensure that your models are transparent, accountable, and ethical.

By following these steps, you can ensure that your machine learning models predict risk for the right population and that they are robust, reliable, and responsible. Machine learning can be a powerful tool for risk management, but it also requires careful planning, analysis, and evaluation to avoid pitfalls and errors.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?