Can Less Be More?

In the age of machine learning (ML), consumers' personal data is widely used for personalized product recommendations. To address privacy concerns, regulations increasingly grant consumers control over their data. One implementation are "opt-out of information use"' features that allow consumers to specify which of their collected personal data ML-powered recommender systems can harness. However, the authors of the paper presented in this ZEW Research Seminar conjecture that such features may have an unintended side effect: withholding data could inadvertently reveal insights about consumers' latent characteristics, thereby enhancing targeting possibilities. Through a controlled pre-registered experiment, they evaluate both consumers' perceptions and technical consequences of such opt-out features in the context of a typical search problem. Their results show that these features increase consumers' sense of control over the system and alleviate privacy concerns for those who actually withhold information. Paradoxically, withholding information can simultaneously be harnessed to improve the ML model's predictive accuracy. From a policy perspective, they highlight the need for additional regulations on how organizations may use information withholding decisions, particularly when consumers' interests conflict with those of the recommender provider.