Representativeness bias occurs when a training dataset systematically over- or under-samples subpopulations relative to the population the deployed system will actually encounter - for example, a facial recognition dataset skewed toward lighter-skinned faces, or a multimodal medical dataset drawn predominantly from one demographic group. Because models learn statistical patterns from their training distribution, an unrepresentative dataset produces a model whose accuracy, calibration, and fairness properties degrade for underrepresented groups, even when aggregate accuracy metrics look acceptable.
This is precisely why aggregate accuracy is an insufficient safeguard: option B's framing - that bias doesn't matter "as long as predictions are accurate" - conflates overall accuracy with subgroup accuracy, and a model can post strong aggregate numbers while systematically failing specific populations. Option D is factually false; AI systems have no inherent neutrality - they inherit and can amplify whatever patterns (including societal biases) exist in their training data and objective function. Option C is also incorrect:
mitigating representativeness bias is significantly cheaper and more effective when addressed at the data- collection and curation stage - through stratified sampling, bias audits, and diverse data sourcing - than after deployment, when it becomes a retraining and remediation problem, and by then real-world harm may have already occurred.
Reference: Trustworthy AI domain - data representativeness, fairness, bias identification and mitigation.