Step 1: Explainability is not just a buzzword, it's a requirement for any self-respecting AI.
Step 2: Understand that explainability is not the same as transparency, although related. Transparency is like, "Hey, I made a decision, but I'm not telling you how." Explainability is like, "Hey, I made a decision, and here's why, and how I made that decision."
Step 3: Choose an explainable AI model, such as a decision tree, or a linear regression.
Step 4: Train your model on a diverse and representative dataset (no, really, just use the whole dataset).
Step 5: Implement a feature importance tool, such as SHAP or LIME, to get an idea of which features are most important.
Learn more about feature importance Understand model diversity, or lack thereof