Summary of Enhancing Model Interpretability with Local Attribution Over Global Exploration, by Zhiyu Zhu et al.
Enhancing Model Interpretability with Local Attribution over Global Exploration
by Zhiyu Zhu, Zhibo Jin, Jiayu Zhang, Huaming Chen
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Local Attribution (LA) algorithm tackles the challenge of model interpretability in artificial intelligence by leveraging local space properties. The LA algorithm consists of targeted and untargeted exploration phases to generate intermediate states for attribution, which thoroughly encompass the local space. This approach achieves an average improvement of 38.21% in attribution effectiveness compared to state-of-the-art methods. Extensive ablation studies validate the significance of each component in the algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence models are often called “black boxes” because their internal mechanisms are unclear. To solve this problem, researchers focus on explaining model decisions using attribution methods. A new approach called Local Attribution (LA) tries to fix this by using local space properties. LA has two parts: targeted and untargeted exploration phases that help create intermediate states for attribution. This makes the results more accurate and reliable. |
