Honestly, I think Interpretability in machine learning refers to the ease with which humans can understand the inner workings of a model's decisions. explainable ML models are specifically engineered to provide clear explanations for their predictions or decisions, aiming to bridge the gap between model complexity and human understanding by offering justifications alongside their outputs.

In the realm of machine learning, the terms "interpretability" and "explainability" are often used interchangeably, but they do carry subtle differences in their connotations and implications.

**Interpretability** generally refers to the ability to understand and make sense of the internal workings or mechanisms of a machine learning model. It's about grasping how the model arrives at its predictions or decisions, often in a human-understandable manner. Interpretable models tend to have simple and transparent structures, such as decision trees or linear regression models, which allow stakeholders to follow the model's reasoning and trust its outputs.

**Explainability** on the other hand, extends beyond just understanding the model's internal workings to providing explicit explanations for its outputs or predictions. Explainable models not only produce results but also offer accompanying justifications or rationales for those results, aimed at clarifying why a certain prediction was made. This could involve highlighting important features, demonstrating decision paths, or providing contextual information that sheds light on the model's reasoning process.

In essence, interpretability is about comprehending the model itself, while explainability is about articulating the model's outputs in a way that is meaningful and useful to human stakeholders. While a model may be interpretable by virtue of its simplicity or transparency, it may not necessarily be explainable if it fails to provide clear justifications for its decisions. Conversely, a complex model may not be easily interpretable, but it can still strive to be explainable by offering insightful explanations for its predictions.

Both interpretability and explainability are crucial aspects of deploying machine learning models in real-world applications, especially in domains where trust, accountability, and regulatory compliance are paramount. By fostering understanding and trust in AI systems, interpretability and explainability pave the way for more responsible and ethical AI adoption, ultimately benefiting both developers and end-users alike.

I am not sure but to my knowledge, while interpretability is understanding how the model works and requires knowledge about parameter tuning and mathematics, explainability is how you justify the decision you take after theevaluation of your models.

I think that there is a clear difference between them. I like the distinct definitions of Interpretability and Explainability: “Interpretability mainly refers to a passive characteristic of a model that makes sense for humans, while the term Interpretability is an active characteristic of a model, denoting any action taken with the intent of clarifying the decision logic” by Riccardo Guidotti.