Artificial intelligence model bias appears when a model consistently favors, omits or misrepresents particular groups, viewpoints or outcomes. It can arise from training data, labeling, optimization goals, evaluation choices and how an application frames the user's options.
Bias cannot be removed through one universal metric because fairness depends on context and affected people. Responsible deployment uses representative evaluation, source transparency, stakeholder review, monitoring and a process for contesting decisions or correcting harmful patterns.
Acronyms and aliases
AI model bias acronymalgorithmic bias synonymartificial intelligence model bias variant
Related terms
Frequently asked questions
Where does artificial intelligence model bias come from?
It can come from historical data, sampling gaps, labels, model objectives, product design, user prompts and the social context in which outputs are used.
How can organizations test for model bias?
They can evaluate representative cases across affected groups, compare outcomes, inspect failure patterns and include domain experts and impacted communities in review.
