The Data-Dollars Tradeoff
Privacy Harms vs. Economic Risk in Personalized AI Adoption
Alexander Erlei (University of Göttingen)
Tahir Abbas (Wageningen University & Research)
Kilian Bizer (University of Göttingen)
Ujwal Gadiraju (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Privacy concerns significantly impact AI adoption, yet little is known about how information environments shape user responses to data leak threats. We conducted a 2 × 3 between-subjects experiment (N = 610) examining how risk versus ambiguity about privacy leaks affects the adoption of AI personalization. Participants chose between standard and AI-personalized product baskets, with personalization requiring data sharing that could leak to pricing algorithms. Under risk (30% leak probability), we found no difference in AI adoption between privacy-threatening and neutral conditions (ca. 50% adoption). Under ambiguity (10-50% range), privacy threats significantly reduced adoption compared to neutral conditions. This effect holds for sensitive demographic data as well as anonymized preference data. Users systematically over-bid for privacy disclosure labels, suggesting strong demand for transparency institutions. Notably, privacy leak threats did not affect subsequent bargaining behavior with algorithms. Our findings indicate that ambiguity over data leaks, rather than only privacy preferences per se, drives avoidance behavior among users towards personalized AI.
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