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XAIVIER: Time Series Classifier Verification with Faithful Explainable AI

Published: 05 April 2024 Publication History

Abstract

Ensuring that a machine learning model performs as intended is a critical step before it can be used in practice. This is commonly done by measuring a model’s predictive performance (e.g., accuracy). However, in high-stakes settings it is often necessary to verify on which data aspects the model actually relies on. This demo presents XAIVIER, the eXplainable AI VIsual Explorer and Recommender, a web application for interactive XAI on time series data. XAIVIER supports dataset exploration and model inspection, allowing users to explain model predictions using various explainer methods. An explainer recommender is provided to advise users which explainer delivers most faithful explanations for their dataset and model. Finally, explanation-based grouping is provided to reveal the model’s underlying decision-making strategies. The proposed set of features aims to cover the full model verification use case for time series classifiers. A demo of XAIVIER is available at https://xai-explorer-demo.know-center.at

Supplemental Material

MP4 File
Demonstration Video

References

[1]
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans. 2019. iNNvestigate Neural Networks!Journal of Machine Learning Research 20, 93 (2019), 1–8. http://jmlr.org/papers/v20/18-540.html
[2]
David Alvarez-Melis and Tommi S. Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 7786–7795.
[3]
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. https://arxiv.org/abs/1909.03012
[4]
Tommy Dang, Huyen N. Nguyen, and Ngan V.T. Nguyen. 2021. VixLSTM: Visual Explainable LSTM for Multivariate Time Series. In Proceedings of the 12th International Conference on Advances in Information Technology (, Bangkok, Thailand,) (IAIT ’21). Association for Computing Machinery, New York, NY, USA, Article 34, 5 pages. https://doi.org/10.1145/3468784.3471603
[5]
Riccardo Guidotti, Anna Monreale, Francesco Spinnato, Dino Pedreschi, and Fosca Giannotti. 2020. Explaining Any Time Series Classifier. In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI). IEEE Computer Society, Los Alamitos, CA, USA, 167–176. https://doi.org/10.1109/CogMI50398.2020.00029
[6]
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, and Alexandru Coca. 2021. Alibi Explain: Algorithms for Explaining Machine Learning Models. Journal of Machine Learning Research 22, 181 (2021), 1–7. http://jmlr.org/papers/v22/21-0017.html
[7]
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020. Captum: A unified and generic model interpretability library for PyTorch. arxiv:2009.07896 [cs.LG]
[8]
Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (2017), 4765–4774.
[9]
Raphael Meudec. 2021. tf-explain. https://doi.org/10.5281/zenodo.5711704
[10]
Nobuyuki Ostu. 1979. A threshold selection method from gray-level histograms.IEEE Trans SMC 9 (1979), 62.
[11]
Hiroaki Sakoe and Seibi Chiba. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing 26, 1 (1978), 43–49.
[12]
Udo Schlegel, Daniela Oelke, Daniel A Keim, and Mennatallah El-Assady. 2023. Visual Explanations with Attributions and Counterfactuals on Time Series Classification. arXiv preprint arXiv:2307.08494 (2023). arxiv:2307.08494 [cs.HC] https://arxiv.org/abs/2307.08494
[13]
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, Italy, 618–626. https://doi.org/10.1109/ICCV.2017.74
[14]
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In Proceedings of the 34th International Conference on Machine Learning - Volume 70(ICML’17). JMLR.org, Sydney, NSW, Australia, 3145–3153.
[15]
Ilija Šimić, Christian Partl, and Vedran Sabol. 2023. XAIVIER the Savior: A Web Application for Interactive Explainable AI in Time Series Data. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP. SCITEPRESS, Lisbon, Portugal, 166–178.
[16]
K Simonyan, A Vedaldi, and Andrew Zisserman. 2014. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. CoRR abs/1312.6 (2014).
[17]
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70(ICML’17). JMLR.org, Sydney, NSW, Australia, 3319–3328.
[18]
Ilija Šimić, Vedran Sabol, and Eduardo Veas. 2022. Perturbation Effect: A Metric to Counter Misleading Validation of Feature Attribution. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 1798–1807. https://doi.org/10.1145/3511808.3557418

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cover image ACM Conferences
IUI '24 Companion: Companion Proceedings of the 29th International Conference on Intelligent User Interfaces
March 2024
182 pages
ISBN:9798400705090
DOI:10.1145/3640544
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 05 April 2024

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Author Tags

  1. Attribution Methods
  2. Clustering
  3. Deep Learning
  4. Explainable AI
  5. Interactive Systems
  6. Recommender
  7. Time Series
  8. Visualization

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  • Demonstration
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  • Refereed limited

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  • ?Bridge? Program of the Austrian Federal Ministry for Climate Action (BMK) and partially funded by Know-Center
  • ?DDAI? COMET Module within the COMET ? Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia

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