Model interpretability in machine learning refers to the ability to explain and understand how a machine learning model makes predictions or decisions. It's an important aspect because as machine learning models become more complex, it becomes increasingly challenging to understand the reasons behind their predictions. This lack of interpretability can be a significant hurdle, particularly in sensitive domains such as healthcare or finance, where the ability to explain model predictions is crucial for gaining trust and ensuring accountability.

There are several techniques and approaches for improving the interpretability of machine learning models:

1. Feature Importance: This technique involves identifying which features or variables have the most significant impact on the model's predictions. Methods like permutation importance, SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations) are commonly used for this purpose.

2. Model-specific Interpretability: Some machine learning models, such as decision trees and linear regression, inherently provide interpretability. Understanding the inner workings of these models can offer insights into how they arrive at predictions.

3. Simplified Models: Creating simpler, interpretable models that approximate the behavior of complex models can provide insight into the decision-making process. Examples include rule-based models and linear models derived from more complex ones.

4. Visualizations: Visualizing model behavior and predictions can aid in understanding how the model is making decisions. This can include visualizing decision boundaries, feature distributions, and prediction uncertainty.

5. Surrogate Models: Building interpretable models that approximate the behavior of complex models can provide insights into their decision-making processes.

6. Explanations: Generating human-readable explanations of model predictions or decisions, such as generating natural language descriptions or rules that describe why a certain prediction was made.

It's essential to note that the choice of interpretability technique depends on the specific machine learning model, the problem domain, and the requirements for interpretability. With the increasing focus on accountability and transparency in machine learning, the development of interpretable models and techniques is an active area of research and development in the field.