Design a plan to ensure AI model fairness across diverse demographic groups.

Example Answer

I would first define the decision the model supports and who could be harmed by a wrong result. Fairness for a recommendation is different from fairness for a decision that restricts access to a service. I would work with domain experts and affected users to choose relevant measures, rather than treating equal overall accuracy as a guarantee.

I would review how the data was collected and compare meaningful errors across groups, including smaller or intersecting groups where the sample supports it. I would report uncertainty and missing coverage. If disparities appeared, I would investigate the data, labels, thresholds, and surrounding workflow, then retest the proposed change. The launch plan would include monitoring, an owner for complaints, and a way to limit or pause harmful behavior. I can reduce and document risks; I cannot promise equitable outcomes simply because a dashboard passes.

Make it your own

Name a specific application, one harm, and the metric that reveals it. Explain what you would do when subgroup data is too limited, and obtain appropriate privacy and legal guidance before collecting sensitive demographic information.

Why this works

It makes fairness a contextual product decision with measurable limits and a response plan.

Follow-up to practice

What if improving one fairness measure worsens another?