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An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks

  • Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to applyDecision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation methods to identify reliable approaches.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/4456
Bibliografische Angaben
Title (English):An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
Conference:Pre-registration workshop NeurIPS (2020), Vancouver, Canada
Author:Udo Schlegel, Daniela OelkeStaff MemberGND, Daniel A. Keim, Mennatallah El-Assady
Date of Publication (online):2020/12/08
Page Number:7
First Page:1
Last Page:7
Parent Title (English):[NeurIPS 2020 Workshops]
URL:https://deepai.org/publication/an-empirical-study-of-explainable-ai-techniques-on-deep-learning-models-for-time-series-tasks
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt
ArXiv Id:http://arxiv.org/abs/2012.04344