Accuracy Standards for AI at Work vs. Personal Life: Evidence from an Online Survey
This research addresses how user expectations for AI reliability vary by context, which is important for AI developers and policymakers designing human-AI interaction systems.
The study examined how people trade off accuracy requirements for AI tools between professional and personal contexts, finding that users demand significantly higher accuracy at work (24.1% requiring high accuracy) than in personal life (8.8%), with a 15.3 percentage point gap. When AI tools are unavailable, respondents reported more disruption in personal routines (34.1%) than at work (15.3%).
We study how people trade off accuracy when using AI-powered tools in professional versus personal contexts for adoption purposes, the determinants of those trade-offs, and how users cope when AI/apps are unavailable. Because modern AI systems (especially generative models) can produce acceptable but non-identical outputs, we define "accuracy" as context-specific reliability: the degree to which an output aligns with the user's intent within a tolerance threshold that depends on stakes and the cost of correction. In an online survey (N=300), among respondents with both accuracy items (N=170), the share requiring high accuracy (top-box) is 24.1% at work vs. 8.8% in personal life (+15.3 pp; z=6.29, p<0.001). The gap remains large under a broader top-two-box definition (67.0% vs. 32.9%) and on the full 1-5 ordinal scale (mean 3.86 vs. 3.08). Heavy app use and experience patterns correlate with stricter work standards (H2). When tools are unavailable (H3), respondents report more disruption in personal routines than at work (34.1% vs. 15.3%, p<0.01). We keep the main text focused on these substantive results and place test taxonomy and power derivations in a technical appendix.