AI Bias.

AI bias occurs when a machine learning system produces systematically prejudiced results due to biased training data, flawed assumptions in model design, or feedback loops that reinforce existing patterns. Bias can manifest as demographic unfairness, cultural assumptions, or consistent factual skew in a particular direction.

Sources of AI Bias

Training data bias is the most common source: if the data used to train a model over-represents certain demographics, viewpoints, or content types, the model learns those biases. Measurement bias (flawed labels), aggregation bias (applying general models to specific groups), and evaluation bias (testing on non-representative samples) also contribute.

Implications for Marketing

AI tools used for audience targeting, content generation, or ad creative can perpetuate biases present in their training data. Marketing teams should audit AI-generated content for cultural assumptions and stereotypes, test ad targeting systems for demographic fairness, and choose vendors who disclose their bias mitigation practices.