Bias

Bias can enter an AI system at multiple stages, including data collection, labeling, model design, training, and deployment. For example, training data that underrepresents certain groups may cause a model to perform differently across populations. Bias can also result from human assumptions, historical patterns, or decisions about which features and metrics a model uses.

Unaddressed bias can lead to unfair decisions, reduced model accuracy, discrimination, and loss of trust in AI systems. Identifying and mitigating bias helps organizations build more reliable and equitable AI while supporting responsible AI practices and regulatory compliance.