LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class

Hongbo Zhu, Angelo Cangelosi, Procheta Sen, Anirbit Mukherjee*

*Corresponding author for this work

Research output: Preprint/Working paperPreprint

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Abstract

The fundamental approach to explanation is to mimic a complex model with a simpler explainer model. Most current work in this field focuses only on explaining the Top predicted class. However, gaining insight into the contributing factors for all potential classes at a particular test point, beyond just the predicted one, can offer valuable insights for machine learning and deep learning practitioners. In this direction, we propose a perturbation-based multi-class explanation model named Locally Interpretable Probabilistic Explanation (LIPEx). LIPEx provides an explanation as a matrix obtained via regression in the space of probability distributions with respect to the squared Hellinger distance. Experiments on both text and image data show that the removal of LIPEx-guided important features from original data causes more prediction degradation of the underlying model than similar tests of other saliency-based or feature importance-based XAI methods. It is also shown that compared to LIME (i.e. state-of-the-art perturbation-based explanation method), LIPEx is more data efficient in terms of using fewer perturbations to obtain a reliable explanation.
Original languageUndefined
Publication statusIn preparation - 7 Oct 2023

Keywords

  • XAI
  • probabilistic models

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • MCAIF: Centre for AI Fundamentals

    Kaski, S. (PI), Alvarez, M. (Researcher), Pan, W. (Researcher), Mu, T. (Researcher), Rivasplata, O. (PI), Sun, M. (PI), Mukherjee, A. (PI), Caprio, M. (PI), Sonee, A. (Researcher), Leroy, A. (Researcher), Wang, J. (Researcher), Lee, J. (Researcher), Parakkal Unni, M. (Researcher), Sloman, S. (Researcher), Menary, S. (Researcher), Quilter, T. (Researcher), Hosseinzadeh, A. (PGR student), Mousa, A. (PGR student), Glover, E. (PGR student), Das, A. (PGR student), DURSUN, F. (PGR student), Zhu, H. (PGR student), Abdi, H. (PGR student), Dandago, K. (PGR student), Piriyajitakonkij, M. (PGR student), Rachman, R. (PGR student), Shi, X. (PGR student), Keany, T. (PGR student), Liu, X. (PGR student), Jiang, Y. (PGR student), Wan, Z. (PGR student), Harrison, M. (Support team), Machado, M. (Support team), Hartford, J. (PI), Kangin, D. (Researcher), Harikumar, H. (PI), Dubey, M. (PI), Parakkal Unni, M. (PI), Dash, S. P. (PGR student), Mi, X. (PGR student) & Barlas, Y. (PGR student)

    1/10/2130/09/26

    Project: Research

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