Because of its ability to find complex patterns in high dimensional and heterogeneous data, machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights. Here, we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics. Machine learning (ML) has emerged as a powerful tool for harnessing big biological data. The complex structure underlying ML models can potentially provide insights into the problems they are used to solve.Because of model complexity, their inner logic is not readily intelligible to a human, hence the common critique of ML models as black boxes.However, advances in the field of interpretable ML have made it possible to identify important patterns and features underlying an ML model using various strategies.These interpretation strategies have been applied in genetics and genomics to derive novel biological insights from ML models.This area of research is becoming increasingly important as more complex and difficult-to-interpret ML approaches (i.e., deep learning) are being adopted by biologists.