As artificial intelligence (AI) applications proliferate, their creators seemingly anticipate that users will make similar trade-offs between costs and benefits across various commercial and public applications, due to the technological similarity of the provided solutions. With a multimethod investigation, this study reveals instead that users develop idiosyncratic evaluations of benefits and costs depending on the context of AI implementation. In particular, the tensions that drive AI adoption depend on perceived personal costs and choice autonomy relative to the perceived (personal vs. societal) benefits. The feeling of being served rather than exploited is strongest for AI directed at infrastructure (cf. commercial AI), due to lower perceived costs. Fears affect AI evaluations, beyond privacy breaches, particularly for surveillance AI, but the fears and costs are less salient for AI directed towards infrastructure. The authors provide guidelines for public policy and AI practitioners, based on how consumers trade off solutions that differ in their benefits, costs, data transparency, and privacy enhancements.
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