Summary of The Fix Benchmark: Extracting Features Interpretable to Experts, by Helen Jin et al.
The FIX Benchmark: Extracting Features Interpretable to eXperts
by Helen Jin, Shreya Havaldar, Chaehyeon Kim, Anton Xue, Weiqiu You, Helen Qu, Marco Gatti, Daniel A Hashimoto, Bhuvnesh Jain, Amin Madani, Masao Sako, Lyle Ungar, Eric Wong
First submitted to arxiv on: 20 Sep 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 FIX (Features Interpretable to eXperts) benchmark and FIXScore measure aim to bridge the gap between feature-based explanation methods and expert knowledge in high-dimensional data. FIXScore is a unified alignment measure applicable to diverse real-world settings, including cosmology, psychology, medicine domains, vision, language, and time series data modalities. The study finds that popular feature-based explanation methods have poor alignment with expert-specified knowledge, emphasizing the need for novel approaches that can better identify features interpretable to experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FIX benchmark and score measure help bridge a gap in understanding between machine learning models and human expertise. It’s hard to find important features when data is high-dimensional, even for experts. The new system helps identify features that are aligned with expert knowledge in different fields like cosmology, psychology, medicine, and more. |
Keywords
* Artificial intelligence * Alignment * Machine learning * Time series
