Model explainability refers to the methods and techniques used to make the predictions of machine learning models understandable to humans. It has become a crucial aspect of AI and data science, especially as models become more complex and often operate as “black boxes.” Users and stakeholders need to trust these models, which is why explainability is no longer just a desirable feature but an essential requirement for safe and reliable AI systems ².

There are various approaches to model explainability, including the use of interpretable models like Logistic Regression and Decision Trees, which are easier to understand but may sacrifice accuracy. In contrast, more complex models like Neural Networks and Random Forests can achieve higher accuracy but are harder to interpret. This trade-off between interpretability and accuracy is a significant challenge in the field ⁴.

Recent advancements, such as Explainable Boosted Machines (EBMs), aim to bridge this gap by providing models that are both interpretable and accurate ⁴. Understanding how models make decisions is vital for ensuring fairness and accountability in AI applications ⁵.