An insurance company's AI model for auto insurance pricing uses telematics data from policyholders' smartphones to calculate risk scores. The model was trained on data from early adopters of the telematics program, who skewed younger, more tech-savvy, and more safety-conscious than the general population. Now the model is applied to all new policyholders. What AI-specific risk does this deployment scenario create?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the training population (self-selected early adopters) is not representative of the general insured population. Early adopters of telematics programs tend to be safer drivers willing to be monitored—training on this group creates a model calibrated to a population with different risk characteristics than the general population.
Full explanation below image
Full Explanation
B is correct because the training population (self-selected early adopters) is not representative of the general insured population. Early adopters of telematics programs tend to be safer drivers willing to be monitored—training on this group creates a model calibrated to a population with different risk characteristics than the general population. Applying it broadly may systematically misprice risk for demographics underrepresented in training (older drivers, less tech-comfortable demographics). A (privacy/consent) is a separate compliance concern. C (technology change) would require model retraining but is not the primary risk identified in the scenario. D (regulatory prohibition) is jurisdiction-specific and not the primary AI risk type described.