Proxy Model Explanations for Time Series RNNs

Zach Wood-Doughty, Isabel Cachola, Mark Dredze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While machine learning models can produce accurate predictions of complex real-world phenomena, domain experts may be unwilling to trust such a prediction without an explanation of the model's behavior. This concern has motivated widespread research and produced many methods for interpreting black-box models. Many such methods explain predictions one-by-one, which can be slow and inconsistent across a large dataset, and ill-suited for time series applications. We introduce a proxy model approach that is fast to train, faithful to the original model, and globally consistent in its explanations. We compare our approach to several previous methods and find both that methods disagree with one another and that our approach improves over existing methods in an application to political event forecasting.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
EditorsM. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages698-703
Number of pages6
ISBN (Electronic)9781665443371
DOIs
StatePublished - 2021
Event20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States
Duration: Dec 13 2021Dec 16 2021

Publication series

NameProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/13/2112/16/21

Keywords

  • Causality
  • Explainability
  • Interpretability
  • Recurrent neural network
  • Time series

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Proxy Model Explanations for Time Series RNNs'. Together they form a unique fingerprint.

Cite this