Local vs. Global Interpretability: A Computational Complexity Perspective
Authors: Shahaf Bassan, Guy Amir, Guy Katz
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a framework for bridging this gap, by using computational complexity theory to assess local and global perspectives of interpreting ML models. We begin by proposing proofs for two novel insights that are essential for our analysis: (i) a duality between local and global forms of explanations; and (ii) the inherent uniqueness of certain global explanation forms. |
| Researcher Affiliation | Academia | 1The Hebrew University of Jerusalem, Jerusalem, Israel. Correspondence to: Shahaf Bassan <shahaf.bassan@mail.huji.ac.il>. |
| Pseudocode | Yes | Algorithm 1 Local Subset Minimal Sufficient Reason |
| Open Source Code | No | The paper does not mention releasing any source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies, therefore no datasets are used or described as publicly available. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments, so no training, validation, or test splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or specific hardware used for its analysis. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers, as it does not report on experiments requiring such details. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, hyperparameters, or training configurations. |