What does "explainability" mean in the context of AI models?

In the context of AI models, "explainability" refers to the capability to understand how a model makes decisions. This is crucial for building trust in AI systems, especially in sensitive applications where transparency is essential, such as healthcare, finance, and law. Explainability involves dissecting the model's decision-making process to reveal the factors and features that influenced a particular prediction or outcome. It allows stakeholders, including developers and end-users, to interpret the AI's reasoning, assess its reliability, and ensure it aligns with ethical standards.

This understanding can also help address biases in AI models, as being able to explain the reasoning behind decisions enables teams to identify and rectify potential unfairness in the algorithms. Overall, improved explainability fosters accountability and facilitates better integration of AI solutions in various domains.