Summary of Maintaining User Trust Through Multistage Uncertainty Aware Inference, by Chandan Agrawal et al.
Maintaining User Trust Through Multistage Uncertainty Aware Inference
by Chandan Agrawal, Ashish Papanai, Jerome White
First submitted to arxiv on: 28 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This research paper proposes a multistage approach to Artificial Intelligence (AI) deployment, where each stage builds upon the previous one with increasing accuracy at the cost of complexity. A method for quantifying model uncertainty is presented, enabling confident deferral decisions. The architecture has been implemented in thousands of cotton farms across India, demonstrating its potential for real-world applications. The paper’s broader implications are relevant to a growing sector of AI deployments in low-resource settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to use Artificial Intelligence (AI) more effectively. Right now, we’re using AI to help cotton farmers in India make better decisions about their crops. The idea is to use multiple steps to get the best results from our AI models. We also came up with a way to measure how sure our AI models are about what they’re saying. This helps us decide when it’s okay to wait for more information before making a decision. This is important because many people in the world need help using AI to improve their lives, but don’t have access to all the resources we do. |
