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Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic shap explanations

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Abstract

With the rapid advancement of artificial intelligence (AI) technology and analytics compute engines, machine learning (ML) models have become increasingly complex. Unfortunately, many of these models are commonly treated as black boxes, lacking user interpretability. As a result, understanding and trusting the predictions made by such complex ML models have become more challenging. However, researchers have developed various frameworks that employ explainable AI methods to enhance the interpretability and explainability of ML models, thereby increasing the trustworthiness of their predictions. In this study, we propose a methodology called Local Interpretable Model Agnostic Shap Explanations (LIMASE). This ML explanation technique leverages Shapley values within the LIME paradigm to achieve several objectives: (a) It explains the prediction of any model by utilizing a locally faithful and interpretable decision tree model. The Tree Explainer is employed to calculate the Shapley values, enabling visually interpretable explanations, (b) It provides visually interpretable global explanations by plotting local explanations for multiple data points, (c) It offers a solution for the submodular optimization problem, (d) It provides insights into regional interpretation, and (e) It enables faster computation compared to the use of kernel explainer. By proposing the LIMASE methodology, this work contributes to the field of ML model interpretability and provides a practical solution to address the challenges posed by complex and opaque ML models. The proposed approach empowers users to gain a deeper understanding of model predictions through visually interpretable explanations at both local and global levels. Overall, this study aims to bridge the gap between the complexity of ML models and the need for interpretability, ultimately enhancing trust and usability in AI-driven applications.

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Authors and Affiliations

  1. ABB Ability Innovation Center, Asea Brown Boveri Company, 10Th Floor, Western Aqua, Kondapur, Hyderabad, 500084, India

    Sai Ram Aditya Parisineni & Mayukha Pal

  2. Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, 502285, India

    Sai Ram Aditya Parisineni

Authors
  1. Sai Ram Aditya Parisineni
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  2. Mayukha Pal
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Contributions

P. Sai Ram Aditya conceived the idea, performed the data analysis, and wrote the manuscript. Mayukha Pal conceptualized the project and contributed to idea generation, results analysis and discussion, guidance, and review of the manuscript.

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Correspondence to Mayukha Pal.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The data obtained for our analysis are from the available public domain database made for academic research purposes.

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Parisineni, S.R.A., Pal, M. Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic shap explanations. Int J Data Sci Anal 18, 457–466 (2024). https://doi.org/10.1007/s41060-023-00458-w

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  • DOI: https://doi.org/10.1007/s41060-023-00458-w

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