Achieving interpretability and explainability can be more challenging in complex machine learning models, such as deep learning models, which often operate as black boxes due to their intricate architectures. It is typically easier to achieve interpretability in simpler models, like decision trees or linear regression, as their inner workings are more transparent. However, through the use of post-hoc explanation methods and visualization techniques, insights into the decision-making process can still be obtained for complex models.