Summary of Data Readiness For Ai: a 360-degree Survey, by Kaveen Hiniduma et al.
Data Readiness for AI: A 360-Degree Survey
by Kaveen Hiniduma, Suren Byna, Jean Luca Bez
First submitted to arxiv on: 8 Apr 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 proposed study aims to develop a standardized framework for evaluating data readiness for artificial intelligence (AI) training, which is crucial for producing accurate and effective AI models. The researchers surveyed over 140 papers from various sources, including ACM Digital Library, IEEE Xplore, Nature, Springer, Science Direct, and online articles by prominent AI experts, to identify existing metrics for verifying data readiness. They intend to propose a taxonomy of data readiness metrics for structured and unstructured datasets, which can lead to new standards for enhancing the quality, accuracy, and fairness of AI training and inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence relies heavily on good-quality data. If the data is bad, the AI won’t work well or even be safe to use. Before using data in AI, it’s important to check its quality. Scientists have been working hard to improve data quality, but they still need a way to measure how ready the data is for AI training. This study looks at over 140 papers and articles from top sources like Nature and Springer to see what metrics are used to check data readiness. The goal is to create a simple framework that can be used to make sure AI data is good quality, accurate, and fair. |
Keywords
» Artificial intelligence » Inference
