Explaining a Series of Models by Propagating Shapley Values

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Chen, Hugh, Lundberg, Scott M, Lee, Su-In
Format: Article
Language:English
Subjects:
Credit bureaus
Extractors
Feature extraction
Gene expression
Linear transformations
Lines of credit
Machine learning
Neural networks
Pipelines
Prediction models
Online Access:Get full text
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Summary:Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution. The latter is particularly important because it often arises in finance where explanations are mandated. Here, we present DeepSHAP, a tractable method to propagate local feature attributions through complex series of models based on a connection to the Shapley value. We evaluate DeepSHAP across biological, health, and financial datasets to show that it provides equally salient explanations an order of magnitude faster than existing model-agnostic attribution techniques and demonstrate its use in an important distributed series of models setting.
ISSN:2331-8422
DOI:10.48550/arxiv.2105.00108