On Thursday, October 24, it is the first ULME seminar this term. As usual we meet at 4:15 pm in room 1.20 (Heho 18). Alicia von Schenk (University of Wuerzburg) gives a talk about “Can Less Be More? Opt-Out Features and Targeting with ML-based Recommender Systems”. 

If you are interested in meeting Alicia in the afternoon or joining us for dinner, please let Sandra know. 

Sandra Ludwig

Please find this term's ULME schedule here.

Abstract:

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, we 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, we evaluate both consumers' perceptions and technical consequences of such opt-out features in the context of a typical search problem. Our 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, we 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.