Summary of Local Vs. Global Interpretability: a Computational Complexity Perspective, by Shahaf Bassan et al.
Local vs. Global Interpretability: A Computational Complexity Perspective
by Shahaf Bassan, Guy Amir, Guy Katz
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Logic in Computer Science (cs.LO)
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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 This research paper proposes a framework for assessing the local and global interpretability of machine learning (ML) models using computational complexity theory. The authors aim to bridge the gap between informal results and lack of mathematical rigor in existing studies. They begin by providing proofs for two novel insights: duality between local and global forms of explanations, and inherent uniqueness of certain global explanation forms. Using these insights, they evaluate the complexity of computing explanations across three model types (linear models, decision trees, and neural networks). The findings offer insights into both local and global interpretability, with results such as linear models being computationally harder to analyze globally than locally, while neural networks and decision trees are harder to analyze locally. This study demonstrates how a computational complexity lens can provide a more rigorous understanding of ML model interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machine learning (ML) models work and why they make certain predictions. The researchers use math and computer science ideas to figure out which parts of the model are most important for making decisions. They look at three different types of models: simple ones that can only learn a few things, more complicated ones that can learn many patterns, and very complex ones that can do almost anything. By using these different models, they show that some ways of understanding why a model made a prediction are easier to do than others. This study helps us understand how ML models work and what makes them good or bad at making decisions. |
Keywords
» Artificial intelligence » Machine learning
