AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Bias in AI is systematic unfairness in a model's outputs, usually inherited from patterns in its training data. Beyond the ethics, biased AI is a legal and reputational liability in hiring, lending, and healthcare. It has to be tested for, because it rarely announces itself.
If every example of 'a good candidate' in your training data was a 45-year-old man, the model learns that pattern, not what actually makes a candidate qualified.
A lending company deploys a credit scoring model that inadvertently penalizes applicants from certain postal codes. The pattern existed in historical data; the model amplified it into a discriminatory outcome.
Bias testing is mandatory before deploying any AI used in hiring, lending, healthcare, or any decision that affects people differently based on protected characteristics.
Bias is not a hypothetical risk to address after launch. Do not deploy high-stakes AI in people-affecting decisions without documented fairness testing and ongoing monitoring in place.
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