  People work with AI systems to improve their decision making, but often
under- or over-rely on AI predictions and perform worse than they would have
unassisted. To help people appropriately rely on AI aids, we propose showing
them behavior descriptions, details of how AI systems perform on subgroups of
instances. We tested the efficacy of behavior descriptions through user studies
with 225 participants in three distinct domains: fake review detection,
satellite image classification, and bird classification. We found that behavior
descriptions can increase human-AI accuracy through two mechanisms: helping
people identify AI failures and increasing people's reliance on the AI when it
is more accurate. These findings highlight the importance of people's mental
models in human-AI collaboration and show that informing people of high-level
AI behaviors can significantly improve AI-assisted decision making.
