How would you approach the challenge of explaining the decision-making process of complex AI models to non-technical stakeholders?

Example Answer

I would first ask what decision the audience needs to make. A support leader may need to know which cases to review, while a privacy owner needs the data flow and its limits. Explain the inputs, predicted output, intended use and consequential errors in that context.

Use a simple diagram and a real permitted example. An analogy can introduce the idea, but I would mark where it stops: a neural network is not literally a committee of departments. Show a correct case and an important failure, and separate the model's score from a downstream human or software action.

If using an explanation technique, state what it explains and its assumptions. Feature attributions describe a model under a chosen method; they are not automatically causal reasons or a faithful verbal account of internal reasoning. Avoid confident stories that the evidence cannot support.

Ask stakeholders to explain when they would rely on the system, when they would escalate and what remains uncertain. That checks understanding more directly than meeting attendance or approval speed. Record unresolved questions and revisit the explanation when the model or workflow changes.