Summary of Dsai: Unbiased and Interpretable Latent Feature Extraction For Data-centric Ai, by Hyowon Cho et al.
DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI
by Hyowon Cho, Soonwon Ka, Daechul Park, Jaewook Kang, Minjoon Seo, Bokyung Son
First submitted to arxiv on: 9 Dec 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 Data Scientist AI (DSAI) framework addresses the issue of large language models relying on pre-trained knowledge rather than actual data patterns. It enables unbiased and interpretable feature extraction through a multi-stage pipeline, utilizing quantifiable prominence metrics to evaluate extracted features. The framework is demonstrated to have high recall in identifying expert-defined features on synthetic datasets with known ground-truth features. Applications on real-world datasets illustrate the practical utility of DSAI in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models struggle to identify hidden characteristics in big data because they rely on pre-existing knowledge rather than actual patterns. To fix this, scientists created a new framework called Data Scientist AI (DSAI). It helps extract important features from data in a fair and understandable way. DSAI uses a series of steps with special metrics to measure the importance of the extracted features. On fake data sets that have known answers, DSAI does well at finding expert-defined features while staying true to the original data. The framework is also useful for real-world projects that need to find meaningful patterns without needing much human oversight. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Recall
