Effects of Generative AI Errors on User Reliance Across Task Difficulty
The capabilities of artificial intelligence (AI) lie along a jagged frontier, where AI systems surprisingly fail on tasks that humans find easy and succeed on tasks that humans find hard. To investigate user reactions to this phenomenon, we developed an incentive-compatible experimental methodology based on diagram generation tasks, in which we induce errors in generative AI output and test effects on user reliance. We demonstrate the interface in a preregistered 3x2 experiment (N = 577) with error rates of 10%, 30%, or 50% on easier or harder diagram generation tasks. We confirmed that observing more errors reduces use, but we unexpectedly found that easy-task errors did not significantl...
| Authors | Jacy Reese Anthis, Hannah Cha, Solon Barocas et al. |
|---|---|
| Primary category | cs.CY |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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