Interpretability refers to the ability to understand and explain the decisions and predictions made by a machine learning model or algorithm. It is an important aspect of machine learning research, as it helps users trust and evaluate the output of a model, as well as identify any biases or errors. Techniques for improving interpretability include feature importance analysis, decision trees, model visualization, and explanations generated by the model itself. Interpretability is particularly crucial in sensitive areas such as healthcare, finance, and criminal justice, where decisions may have significant consequences.