Problems with Shapley-value-based explanations as feature importance measures
Authors: I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, Sorelle Friedler
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we demonstrate that Shapley-value-based explanations for feature importance fail to serve their desired purpose in general. We make this argument in two parts. Firstly, we show that applying the Shapley value to the problem of feature importance introduces mathematically formalizable properties which may not align with what we would expect from an explanation. Secondly, taking a human-centric perspective, we evaluate Shapley-value-based explanations through established frameworks of what people expect from explanations, and find them wanting. |
| Researcher Affiliation | Academia | 1School of Computing, University of Utah, Salt Lake City, UT, USA 2Department of Computer Science, University of Arizona, Tucson, AZ, USA 3Department of Computer Science, Haverford College, Haverford, PA, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for a methodology described by the authors, as it is a theoretical critique rather than a new system implementation. |
| Open Datasets | No | The paper is a theoretical analysis and does not involve training models on datasets, thus it does not provide concrete access information for a publicly available or open dataset for its own work. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so it does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details or hyperparameters. |