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short-paper

Speeding up LIME using Attention Weights

Authors:

Sudarsun Santhiappan,

Mukesh Kumar Reghu,

Mahendran Saminathan,

Aakash VeerappanAuthors Info & Claims

CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

Pages 444 - 448

https://doi.org/10.1145/3632410.3632450

Published: 04 January 2024 Publication History

Abstract

LIME (Local Interpretable Model-Agnostic Explanations), a model-agnostic framework for eXplainable AI (XAI), has emerged as a powerful technique for generating instance-level explanations. However, the computational cost of LIME can be prohibitively high, especially while dealing with large datasets or complex models. This work proposes a novel approach to speed up the LIME algorithm by leveraging attention weights from an upstream classification task. The per-label attention mechanism allows a classification model to focus on different labels independently and learn label-specific attention weights. By utilizing the attention weights of a target label, we aim to restrict the perturbable tokens, thereby reducing the number of perturbations and inference time required by LIME. Experiments on open-source datasets demonstrate a minimum 50% speed improvement in explanation generation, preserving over 85% of LIME’s original explanations.

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Cited By

Nugraha BJnanashree ABauschert T(2024)An Efficient Explainable Artificial Intelligence (XAI)-Based Framework for a Robust and Explainable IDS2024 8th Cyber Security in Networking Conference (CSNet)10.1109/CSNet64211.2024.10851760(173-181)Online publication date: 4-Dec-2024
https://doi.org/10.1109/CSNet64211.2024.10851760

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CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

January 2024

627 pages

ISBN:9798400716348

DOI:10.1145/3632410

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 January 2024

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CODS-COMAD 2024

CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

January 4 - 7, 2024

Bangalore, India

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Cited By

Nugraha BJnanashree ABauschert T(2024)An Efficient Explainable Artificial Intelligence (XAI)-Based Framework for a Robust and Explainable IDS2024 8th Cyber Security in Networking Conference (CSNet)10.1109/CSNet64211.2024.10851760(173-181)Online publication date: 4-Dec-2024
https://doi.org/10.1109/CSNet64211.2024.10851760

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