Summary of A Decision-driven Methodology For Designing Uncertainty-aware Ai Self-assessment, by Gregory Canal et al.
A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment
by Gregory Canal, Vladimir Leung, Philip Sage, Eric Heim, I-Jeng Wang
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel suite of tools has been developed to quantify the uncertainty of Artificial Intelligence (AI) predictions, enabling AI systems to “self-assess” their reliability. This manuscript categorizes methods for AI self-assessment along key dimensions and provides guidelines for selecting and designing suitable approaches for practitioners. The focus is on uncertainty estimation techniques that consider the impact of self-assessment on downstream decision-making and subsequent costs and benefits. To demonstrate the methodology’s utility, two realistic national-interest scenarios are illustrated. This guide is intended for machine learning engineers and AI system users seeking to select ideal self-assessment techniques for specific problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI has come a long way in making decisions and systems more efficient. However, there are many situations where we can’t fully trust the predictions made by these AI systems. To make things better, a group of tools has been created to figure out how reliable an AI’s prediction is. This helps us design better AI systems that can assess themselves. The article explains different ways to do this and provides tips on choosing the right method for each problem. It also shows how these methods can be used in real-life scenarios. |
Keywords
* Artificial intelligence * Machine learning
