Summary of To Err Is Ai! Debugging As An Intervention to Facilitate Appropriate Reliance on Ai Systems, by Gaole He et al.
To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems
by Gaole He, Abri Bharos, Ujwal Gadiraju
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A proposed debugging intervention aimed at fostering “appropriate reliance” on AI systems in human-AI collaboration is explored in this paper. The authors argue that accurately estimating the trustworthiness of AI advice is crucial, but challenging, especially without performance feedback. They draw inspiration from critical thinking literature and propose a debugging approach to calibrate users’ assessments. A quantitative study with 234 participants found that the intervention did not lead to increased reliance on the AI system, but rather a decrease, potentially due to early exposure to its weaknesses. The authors investigate user confidence and AI trustworthiness across groups with different performance levels to explain how inappropriate reliance patterns emerge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems can help humans make better decisions, but it’s hard to know when an AI is giving good advice without feedback. Researchers tried to fix this by making people think about how an AI system works. They thought this would help people trust the AI more, but instead, people started trusting the AI less after seeing its mistakes. This might happen because people are surprised when they see an AI make a mistake and lose confidence in it. |
