Data/ML/AI
Explainable AI
Also written as XAI, Model Interpretability, Model Explainability, SHAP, LIME
Techniques for showing why a model made a particular decision, such as which factors pushed a loan application towards 'reject'. SHAP and LIME are the most common tools. Increasingly expected wherever AI decisions affect people, including hiring and credit.
Think of it like
A teacher who shows the working, not just the final answer.
Junior or senior?
Matters most in regulated industries and anywhere a customer or regulator can ask 'why?'.
Senior sounds like
Has explained a model's behaviour to a non-technical stakeholder and changed something because of what the explanation revealed.
Ask them
“Tell me about a time an explanation of a model's decisions showed you something was wrong. What did you change?”