What is the difference between explainable and interpretable machine learning?

Answer: Explainable machine learning focuses on providing post-hoc explanations for model predictions, while interpretable machine learning emphasizes inherent simplicity and understandability in the model structure.

1. Explainable Machine Learning (XAI):
   • Focuses on the ability to provide post-hoc explanations for model predictions.
   • It aims to make the inner workings of a model more transparent and understandable to users or stakeholders after the model has made a prediction.
   • Typically involves generating explanations or justifications for specific model outputs.
2. Interpretable Machine Learning:
   • Emphasizes the inherent transparency and simplicity of the model itself.
   • The goal is to build models that are inherently easier to understand by design, without relying on additional post-hoc explanations.
   • Interpretable models are usually simpler, such as decision trees or linear models, making them more straightforward for humans to grasp.

Key difference between explainable and interpretable machine learning are:

Aspect	Explainable Machine Learning	Interpretable Machine Learning
Definition	Focuses on providing post-hoc explanations for model predictions, often using methods like feature importance or attention mechanisms.	Emphasizes inherent simplicity and understandability in the model structure, enabling straightforward comprehension without additional explanations.
Goal	Aims to make complex models understandable and transparent to end-users or stakeholders after the model has made predictions.	Strives to build models with inherently transparent structures that are easy to interpret without the need for additional explanations.
Methods	Frequently utilizes techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), or attention maps.	Focuses on using simpler, transparent algorithms such as decision trees or linear models, which inherently offer interpretability.
Trade-offs	May sacrifice some simplicity for accuracy, and the explanations may not align with human intuition.	Tends to prioritize simplicity and may sacrifice a degree of predictive performance for the sake of an easily interpretable model.
Applicability	Often preferred in complex models like deep neural networks where understanding the decision-making process is challenging.	Suitable for scenarios where a clear, easily understandable model is crucial, such as in regulatory or high-stakes applications.

Conclusion:

In conclusion, while explainable machine learning aims to provide post-hoc insights into complex models, interpretable machine learning focuses on building inherently transparent models. The choice between the two depends on the specific requirements of a given application, balancing the need for accuracy with the importance of model transparency and ease of understanding.