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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.
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This content will become publicly available on November 1, 2025
Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI
In this multisite prospective study of simulated artificial intelligence (AI)–assisted chest radiograph diagnosis involving 220 physicians, AI explanation type (local vs global) differentially impacted physician diagnostic performance and trust in AI advice.
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- Award ID(s):
- 1840088
- PAR ID:
- 10584719
- Publisher / Repository:
- Radiological Society of North America
- Date Published:
- Journal Name:
- Radiology
- Volume:
- 313
- Issue:
- 2
- ISSN:
- 0033-8419
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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