Summary of Explainability Through Uncertainty: Trustworthy Decision-making with Neural Networks, by Arthur Thuy et al.
Explainability through uncertainty: Trustworthy decision-making with neural networks
by Arthur Thuy, Dries F. Benoit
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 paper proposes a general uncertainty framework for machine learning models, focusing on neural networks that tend to be overconfident. The authors highlight the importance of uncertainty estimation in communicating when model outputs should not be trusted. They bridge the gap between methods for uncertainty estimation and the field of explainable artificial intelligence (XAI), positioning uncertainty as an XAI technique providing local and model-specific explanations. The framework consists of three components: uncertainty estimation, classification with rejection, and a case study on neural networks in educational data mining subject to distribution shifts. By integrating human expertise into uncertain observations, the authors aim to create more actionable and robust machine learning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence (AI) models more trustworthy by figuring out when they’re not sure about something. Right now, AI models are often too confident, which is a problem when things change unexpectedly. The researchers developed a new framework that combines several techniques to help AI models be less overconfident and more accurate. They applied this framework to a specific area of study – using AI to analyze educational data – to show how it can make AI systems more reliable and helpful in real-life decisions. |
Keywords
* Artificial intelligence * Classification * Machine learning
