Unintended Consequences of Algorithmic Personalization

“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for algorithmic biases in marketing interventions, encompassing promotion, product, price, and distribution. The case is designed to enhance students' understanding of algorithmic bias in personalized marketing. It encourages discussions on its causes and strategies for detection and mitigation. A key learning is that such bias is often unintentional and can occur without data errors or underrepresentation in the sample. A central theme is the trade-off between optimization and fairness in algorithmic decision-making. Overall, these case studies provide comprehensive discussions on the causes, implications, and solutions to algorithmic bias in personalized marketing, complemented by the technical note “Algorithm Bias in Marketing” (HBS No. 521-020) that accompanies the case.