The Data-Dollars Tradeoff: Privacy Harms vs. Economic Risk in Personalized AI Adoption

TL;DR

This study investigates how privacy risk perceptions, especially ambiguity about data leaks, influence user adoption of AI personalization, revealing that ambiguity reduces adoption and transparency increases privacy demand.

Contribution

It provides empirical evidence that ambiguity about privacy leaks, rather than known risks, significantly impacts AI adoption decisions, highlighting the importance of transparency.

Findings

Ambiguity about data leaks reduces AI adoption.

Users overvalue privacy labels, indicating demand for transparency.

Privacy threats do not alter bargaining behavior with algorithms.

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 x 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.