Summary of Ai Data Readiness Inspector (aidrin) For Quantitative Assessment Of Data Readiness For Ai, by Kaveen Hiniduma et al.
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
by Kaveen Hiniduma, Suren Byna, Jean Luca Bez, Ravi Madduri
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 “Garbage In Garbage Out” proposes a framework, AIDRIN (AI Data Readiness Inspector), to quantify and evaluate the quality of data for artificial intelligence processes. The authors define parameters of AI data readiness, covering various dimensions such as completeness, outliers, duplicates, feature importance, correlations, class imbalance, fairness, privacy, and FAIR principle compliance. AIDRIN uses metrics from traditional data quality assessment and those specific to assessing data for AI applications. The framework provides visualizations and reports to aid data scientists in evaluating data readiness for machine learning pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI experts have a problem: they waste time preparing bad data for their models. But there’s no way to know if the data is good or not. This paper tries to fix that by creating a system called AIDRIN, which looks at many different things to see how ready the data is for AI. It checks for things like missing information, weird values, and fairness. The system gives results in a special report, so data scientists can make better decisions about what data to use. |
Keywords
» Artificial intelligence » Machine learning
