Explainability
The degree to which the internal mechanics, logic, and decision-making processes of a machine learning model can be understood and interpreted by human experts.
Think of It Like This
Like showing your work on a complex math test rather than just writing down the final answer, so the teacher knows exactly how you solved the problem.
Explainability is crucial for building trust in AI systems, especially in highly regulated fields like healthcare and finance. While simple models like decision trees are naturally explainable, deep neural networks function as opaque black boxes. Techniques are required to extract post-hoc explanations for why these complex models make specific predictions.