Explaining AI Decisions to Non-Technical Users

Opening Scene

A doctor holds an X-ray up to the light and points, with a finger, directly at the hairline fracture running across the bone. There is no talk of Hounsfield units or radiographic density; there’s a plain sentence, something like, this is the break, and this is why your wrist hurts, delivered in language built for the person standing there, not for another radiologist. The patient leaves understanding something real, not just having been shown an image.

In Plain English

Explaining AI decisions to non-technical users means translating a technical explanation, whether a SHAP value, a feature importance ranking, or a model confidence score, into plain, actionable language a layperson can genuinely understand and, where relevant, act on. This is a distinct skill from producing the underlying technical explanation in the first place, and it deserves its own deliberate design attention: an explanation aimed at an auditor and an explanation aimed at the person actually affected by a decision are not the same document.

The Old Way

Before this translation step was treated as its own discipline:

Technical explanations, when given at all, were often just raw output, numbers and jargon, dumped directly on users.
There was little design attention paid to translating model reasoning for a general audience rather than a technical one.
Users receiving an automated decision often had no accessible way to actually understand or meaningfully contest it.

Treating this translation as a genuine design problem is what closes that gap.

What’s Changing (and Why AI Is the Reason)

UX and communication design are becoming genuine disciplines within explainability work, not an afterthought bolted onto the technical output at the last minute.
This connects to the plain-language translation principles covered in this content library’s dedicated data storytelling and narrative series, applied here to model output instead of dashboards and reports.
As consumer-facing AI, including chatbots, recommendation systems, and credit decisions, reaches more ordinary users, getting this translation right has real stakes for millions of people who will never read a SHAP value directly.

The Metaphor, Fully Extended

Holding the Film to the Light and Pointing at the Fracture	Translating a Technical Explanation Into Plain Language
A doctor speaking without medical jargon	A model output translated without statistical jargon
The patient leaving understanding what’s wrong and why	The user leaving understanding the decision and why
Enough detail to trust it, not so much it overwhelms	Enough detail to trust it, not so much it overwhelms
Language chosen for the person in the room	Language chosen for the person receiving the decision

For Beginners: What to Actually Do

Practice rewriting a technical explanation, such as a stated feature contribution value, into one plain sentence a non-expert would understand.
Notice good and bad examples of this translation in apps and services you already use.
Learn that over-explaining can be just as unhelpful as under-explaining; both bury the point that actually matters.

For Practitioners and Leaders: The Deeper Layer

User-test explanation language with actual non-technical users, not just internal reviewers who already understand the underlying model.
Design explanations around the specific action a user needs to take next, not just transparency for its own sake.
Borrow structure from the plain-language translation techniques covered in this content library’s dedicated data storytelling and narrative series when writing user-facing explanation copy.

Quick Recap

Explaining AI decisions to end users requires translating technical output into genuinely plain language.
This is a distinct discipline from producing the underlying technical explanation itself.
Historically, this translation step was frequently skipped altogether.
Growing consumer-facing AI use makes getting it right increasingly consequential.

Where This Fits in the Series

Article 9 covered the legal right to an explanation; this article covered actually delivering one a person can use. Article 11 raises the stakes further, into the domains where getting this translation right matters most: medicine, credit, and hiring.