Summary of Can I Trust My Fake Data — a Comprehensive Quality Assessment Framework For Synthetic Tabular Data in Healthcare, by Vibeke Binz Vallevik et al.
Can I trust my fake data – A comprehensive quality assessment framework for synthetic tabular data in healthcare
by Vibeke Binz Vallevik, Aleksandar Babic, Serena Elizabeth Marshall, Severin Elvatun, Helga Brøgger, Sharmini Alagaratnam, Bjørn Edwin, Narasimha Raghavan Veeraragavan, Anne Kjersti Befring, Jan Franz Nygård
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper addresses the challenge of ensuring safe adoption of AI tools in healthcare, particularly with regards to synthetic data (SD). Synthetic data is created by training a generator on real data to produce a dataset with similar statistical properties. However, there is currently a lack of standardized quality evaluation metrics for SD, leading to a complex landscape. The authors conducted a comprehensive literature review and developed a conceptual framework for quality assurance of SD, incorporating dimensions such as fairness, carbon footprint, and statistical similarity. This framework aims to provide assurance for safe and responsible real-life applications of SD. By increasing transparency and reducing safety risks, the paper highlights the importance of building trust in synthetic data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that AI tools are safe to use in healthcare. One way to do this is by using fake data (synthetic data) instead of real patient information. But there’s a problem – there isn’t an agreed-upon way to measure the quality of synthetic data. The authors looked at what other researchers have said about this topic and came up with a framework for making sure that synthetic data is good enough to use in real-life applications. This framework includes things like fairness, which means making sure that AI tools don’t discriminate against certain groups of people, and carbon footprint, which refers to the environmental impact of using these tools. |
Keywords
* Artificial intelligence * Synthetic data
