Sampling Bias

Sampling Bias arises when training data fails to represent the population the AI will later serve. The classic example is the 1936 Literary Digest poll, which predicted Alf Landon would defeat FDR because the sample (drawn from telephone and automobile owners) skewed wealthy — and Roosevelt won in a landslide. In modern AI, sampling bias appears when image classifiers are trained on Western photographs and fail in other cultural contexts, when health AI is validated on academic-medical-center patients and misfires in community clinics, or when language models reflect the demographics of internet contributors rather than the broader population. Detection requires comparing training-data demographics to deployment-population demographics. Mitigation includes targeted data collection from underrepresented groups, reweighting samples, and explicitly evaluating performance per subgroup. AI governance and AI compliance frameworks require sampling-bias analysis as part of fairness review, supporting AI ethics and responsible AI deployment for every AI system that affects people across diverse populations and contexts.

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