Target developed a pregnancy prediction algorithm using customer purchase data that accurately identified pregnant customers and sent them targeted coupons, raising privacy concerns when it revealed a teenager's pregnancy to her father.
Target developed a predictive analytics system that analyzed customer purchase patterns to identify pregnant women and send them targeted baby-related coupons. The system, created by statistician Andrew Pole around 2002, analyzed purchasing data from women who had signed up for baby registries and identified about 25 products that, when purchased together, could predict pregnancy with high accuracy. The algorithm assigned each customer a 'pregnancy prediction score' and estimated due dates. In one notable incident, the system sent baby-related coupons to a high school student, prompting her angry father to complain to store management before later discovering his daughter was indeed pregnant. Target refined their approach by mixing baby product ads with unrelated items to make the targeting appear random and avoid making customers uncomfortable about being tracked. The system was part of Target's broader effort to capture customers during life transitions when shopping habits are most flexible.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed