Machine learning (ML) can have a significant impact on public policy by modelling complex relationships and augmenting human decision-making. However, overconfidence in results and incorrectly interpreted algorithms can lead to peril, such as the perpetuation of structural inequities. In this report, the authors give an overview of ML and discuss the importance of its interpretability. In addition, they offer the following recommendations, which will help policy-makers develop trustworthy, transparent and accountable information that leads to more-objective and more-equitable policy decisions:

1. improve data through coordinated investments;
2. approach ML expecting interpretability, and be critical; and
3. leverage interpretable ML to understand policy values and predict policy impacts.