How do you explain a model's results to stakeholders who do not have a technical background?

Translate into decisions and costs, and never lead with the algorithm.

• Frame performance in business terms. Not "87% accuracy" but "of the 100 customers it flags each month, about 70 genuinely are at risk, and it catches roughly half of everyone who leaves". Precision and recall become meaningful when expressed as consequences.
• Be explicit about the two kinds of error and what each costs. A false positive on a fraud model means inconveniencing a real customer; a false negative means losing money. The threshold is a business decision, not a technical one, and framing it that way is what earns trust.
• Use feature importance or SHAP to explain drivers, but be careful to describe them as associations rather than causes — stakeholders will act on them as causes otherwise.
• State the limits plainly. What the model does not know, where it should not be relied on, and how it may degrade.

Note: Being willing to recommend a simpler, more interpretable model when the stakes require explanation — credit decisions, medical triage — shows maturity. Accuracy is not the only requirement.