Representation problems begin with data: if training data under-represents certain genders, ethnicities, ages, accents, disabilities, or regions, the system may perform worse for them or treat them as exceptions. 

It also includes design choices, such as whose needs are prioritised, which languages are supported, and whether people affected by the system had any input into how it was built.

Representation also affects outputs. An AI system can mislabel people, present stereotyped images or text, or treat one group as the default “normal” user while others are handled poorly or invisibly. 

In automation, the same issue appears when workflows, forms, or decision rules assume a narrow range of bodies, identities, work patterns, or life circumstances.

Why it matters

Representation matters because AI and automation increasingly shape access to jobs, healthcare, education, media, finance, and public services. Missing or misrepresented groups can receive worse outcomes, less accurate decisions, or fewer opportunities.

It also matters socially because repeated distortions can normalise stereotypes and weaken trust in AI systems and institutions. In the long run, poor representation can make automation less reliable, less inclusive, and less legitimate.

Impacts & consequences

Causes & contributing factors

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