How do you ensure explainability and transparency of AI and ML models and decisions?
AI and ML models are powerful tools for solving complex problems, but they also pose challenges for ensuring explainability and transparency of their decisions. Explainability refers to the ability to understand how and why a model makes a prediction or recommendation, while transparency refers to the ability to access and verify the data, algorithms, and processes behind a model. Both are essential for building trust, accountability, and ethical standards in AI and ML applications. In this article, you will learn some of the methods and best practices for ensuring explainability and transparency of AI and ML models and decisions.