Summary of Trustworthy Artificial Intelligence in the Context Of Metrology, by Tameem Adel et al.
Trustworthy Artificial Intelligence in the Context of Metrology
by Tameem Adel, Sam Bilson, Mark Levene, Andrew Thompson
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The abstract reviews National Physical Laboratory (NPL) research on trustworthy artificial intelligence (TAI), specifically trustworthy machine learning (TML), in the context of metrology. It highlights three broad themes: technical, socio-technical, and social, emphasizing uncertainty quantification (UQ) for transparency and trust. The paper discusses NPL’s work on three TAI research areas, including AI system certification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make sure artificial intelligence is trustworthy. Scientists at the National Physical Laboratory are working on this problem. They want to be able to rely on AI systems to make good decisions. To do this, they need to figure out how to measure and quantify uncertainty in AI models. This will help us understand what’s going on inside these complex systems. |
Keywords
* Artificial intelligence * Machine learning
