Most teams say their AI is "human-centred". Far fewer can point to what that actually means in the product. Human-centred AI (HCAI) isn't a values statement. It's a set of design decisions you can see, test and measure.

The idea comes from researchers like Ben Shneiderman, who argued that we don't have to choose between automation and human control. The best systems give you both: high automation for routine work and clear human control when it matters.

Here are six patterns that make the difference in practice.

1. Keep a human on the decisions that matter

Not every decision needs a person. But decisions that affect someone's money, health, job or legal position usually do.

In practice: the AI recommends, a person approves or overrides, and every override is logged and fed back to improve the system. A loan tool that auto-approves low-risk cases but routes edge cases to an underwriter is a good example.

2. Show how sure the system is

A model that presents every answer with the same confidence trains people to trust it blindly, or to stop trusting it at all.

In practice: surface confidence in plain language ("low confidence, please check"), and change the UI for uncertain cases. Doctors, analysts and support agents use AI tools far better when they know when not to rely on them.

3. Make it easy to correct and easy to opt out

If fixing a wrong suggestion takes a support ticket, people will either ignore the AI or let errors through.

In practice: one-click corrections, an "undo" for AI actions, and a way to switch the AI off for a task. Corrections are also your best source of improvement data.

4. Explain in the user's terms

A feature-importance chart is not an explanation for a customer service agent.

In practice: explanations tied to the task: "Flagged because this invoice is 4x the supplier's usual amount." Short, specific, and something the user can act on or dispute.

5. Design with the people affected, not just the buyer

The people who approve an AI purchase are rarely the people who live with its decisions.

In practice: involve frontline users and affected groups early, in research, testing and pilots. Many AI failures are failures of fit: the model works, but the workflow around it doesn't.

6. Measure human outcomes, not just model accuracy

A model can be 95% accurate and still make people slower, more stressed or less careful.

In practice: track time saved, errors caught, override rates and user trust alongside accuracy. If override rates are near zero, check whether people are really reviewing or just clicking "accept".

A one-question test

For any AI feature you're building or buying, ask:

If it's wrong, who notices, and how easily can they fix it?

If the answer is "nobody" or "with difficulty", the system isn't human-centred yet, however good the model is.

Want to go deeper?

Our Human-Centered AI (HCAI) Mastery Course covers these patterns with real cases, plus how to apply them across the AI lifecycle: research, design, evaluation and governance.

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Which of these patterns is hardest to get right in your organisation? Share in the comments.

— Team SkillMint