AI Bias

Also known as: Algorithmic Bias, Model Bias, AI Fairness, Discriminatory AI

Systematic errors in AI model outputs caused by biases in training data or algorithm design that lead to unfair or inaccurate results.

AI Bias refers to systematic, non-random errors in the outputs of artificial intelligence models that result in differential or incorrect treatment of groups of people, originating from biases present in training data or algorithmic design decisions.

In market research, AI bias is especially critical because it can: (1) distort results if training data overrepresents certain demographic segments; (2) reproduce cultural or gender biases in verbatim coding; (3) generate synthetic panels that do not adequately represent minorities or lower-income segments; (4) produce sentiment analysis that incorrectly interprets Spanish from different Latin American regions.

Mitigation measures include: regular model audits with representative evaluation data, documentation of known limitations, human oversight of critical outputs, and the use of diverse and inclusive training data.

Atlantia maintains bias detection and mitigation protocols in all its AI models applied to research.