Organizations: Stanford University, Stanford, California, USA · University of Chicago, Chicago, Illinois, USA · Microsoft Research, New York, New York, USA
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 significantly reduce use more than hard-task errors, suggesting that people are not averse to jaggedness in this experimental setting. We encourage future work that varies task difficulty at the same time as other features of AI errors, such as whether the jagged error patterns are easily learned.
Figures & tables
Figure 1. Screenshots of the study interface. In Phase 1 (left), participants predict whether the AI tool will successfully generate the diagram. In Phase 2 (right), participants report their WTP for an opportunity to use the AI tool for a monetary reward. Two screenshots of the study interface are shown side-by-side. In Phase 1 (left), participants see a sketch of the target diagram and prompt, and they predict whether the AI tool will successfully generate the diagram. This participant correctly predicted that the diagram would be successfully generated. In Phase 2 (right), participants report their WTP for an opportunity to use the AI tool for a monetary reward. This participant typed in 12 cents for a relatively simple diagram.
Figure 2. Bids across the six experimental conditions. Means are model-adjusted, and error bars show standard errors. The violin plot shows distributions for each of the six experimental conditions.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 3. The easiest (i.e., simplest) (A) and hardest (i.e., most complex) (B) diagrams that participants were asked to recreate in Phase 2. The figure shows an example of an easy task on the left labeled as "A" and a hard task on the right labeled as "B".
Figure 4. First stages of the study interface. This participant was randomly assigned to view the Phase 1 tasks in ascending order of difficulty (easier to harder) and to see errors later in the phase (for the more difficult tasks, not shown here). The Phase 2 task order is fully randomized.
Figure 5. Additional stages of the study interface. This participant was randomly assigned to view the Phase 1 tasks in ascending order of difficulty (easier to harder) and to see errors later in the phase (for the more difficult tasks, not shown here). The Phase 2 task order is fully randomized.
Figure 6. Additional stages of the study interface. This participant was randomly assigned to view the Phase 1 tasks in ascending order of difficulty (easier to harder) and to see errors later in the phase (for the more difficult tasks, not shown here). The Phase 2 task order is fully randomized.
Figure 7. Participants were asked how many attempts they believed it would take to successfully generate the diagram in each Phase 2 task. Each task had more nodes or more complex connections than lower-number diagrams. In particular, Task 2 had one more node than Task 1, but Task 2 was sequential while Task 1 was cyclic, which could explain why Task 2 was viewed as easier. The task difficulties were selected in order to have two easier tasks, four tasks of middling difficulty, and two harder tasks. Tasks were displayed in randomized order.
Figure 8. The empirical bid distribution for participants across the six experimental conditions, separated by prior AI consumption (heavy, moderate, and low). Error bars show standard errors. Three violin plots for three separate groups of participants (heavy, moderate, and low AI-content consumers), each with six violin distributions, one for each experimental condition.