AI on the street: context-dependent responses to artificial intelligence

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 tension between being served rather than exploited is lowest for public AI directed at infrastructure (cf. commercial AI), due to lower perceived costs. Surveillance AI evaluations are driven by fears beyond mere privacy breaches, which overcome the societal and safety benefits. Privacy-breaching applications are more acceptable when public entities implement them (cf. commercial). 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.