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AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases

Published: 04 August 2023 Publication History

Abstract

With the growing adoption of AI, trust and explainability have become critical which has attracted a lot of research attention over the past decade and has led to the development of many popular AI explainability libraries such as AIX360, Alibi, OmniXAI, etc. Despite that, applying explainability techniques in practice often poses challenges such as lack of consistency between explainers, semantically incorrect explanations, or scalability. Furthermore, one of the key modalities that has been less explored, both from the algorithmic and practice point of view, is time-series. Several application domains involve time-series including Industry 4.0, asset monitoring, supply chain or finance to name a few.
The AIX360 library (https://github.com/Trusted-AI/AIX360) has been incubated by the Linux Foundation AI & Data open-source projects and it has gained significant popularity: its public GitHub repository has over 1.3K stars and has been broadly adopted in the academic and applied settings. Motivated by industrial applications, large scale client projects and deployments in software products in the areas of IoT, asset management or supply chain, the AIX360 library has been recently expanded significantly to address the above challenges. AIX360 now includes new techniques including support for time-series modality introducing time series based explainers such as TS-LIME, TS Saliency explainer, TS-ICE and TS-SHAP. It also introduces improvements in generating model agnostic, consistent, diverse, and scalable explanations, and new algorithms for tabular data.
In this hands-on tutorial, we provide an overview of the library with the focus on the latest additions, time series explainers and use cases such as forecasting, time series anomaly detection or classification, and hands-on demonstrations based on industrial use-cases selected to demonstrate practical challenges and how they are addressed. The audience will be able to evaluate different types of explanations with a focus on practical aspects motivated by real deployments.

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  • (2024)Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applicationsArtificial Intelligence Review10.1007/s10462-024-11005-958:1Online publication date: 8-Nov-2024

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    cover image ACM Conferences
    KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2023
    5996 pages
    ISBN:9798400701030
    DOI:10.1145/3580305
    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: 04 August 2023

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    Author Tags

    1. artificial intelligence
    2. explainability
    3. industry 4.0
    4. time-series

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    • (2024)Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applicationsArtificial Intelligence Review10.1007/s10462-024-11005-958:1Online publication date: 8-Nov-2024

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