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International Conference on Discovery Science

DS 2022: Discovery Science pp 525–540Cite as

Interpretable Latent Space to Enable Counterfactual Explanations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13601))

Abstract

Many dimensionality reduction methods have been introduced to map a data space into one with fewer features and enhance machine learning models’ capabilities. This reduced space, called latent space, holds properties that allow researchers to understand the data better and produce better models. This work proposes an interpretable latent space that preserves the similarity of data points and supports a new way of learning a classification model that allows prediction and explanation through counterfactual examples. We demonstrate with extensive experiments the effectiveness of the latent space with respect to different metrics in comparison with several competitors, as well as the quality of the achieved counterfactual explanations.

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Notes

  1. 1.

    For the convergence problem, we used the early stopping technique.

  2. 2.

    ILS code; UCI and pytorch datasets; PCA, UMAP, and TMAP methods links.

  3. 3.

    TMAP crashed for \(k>10\) due to the exponential computational cost.

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Acknowledgment

This work has been partially supported by the European Community Horizon 2020 program under the funding schemes: H2020-INFRAIA-2019–1: Research Infrastructure GA 871042 SoBigData++, G.A. 952026 HumanE-AI Net, ERC-2018-ADG GA 834756 XAI: Science and technology for the eXplanation of AI decision making, G.A. 952215 TAILOR.

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Authors and Affiliations

  1. Scuola Normale Superiore, Piazza dei Cavalieri, 7, Pisa, Italy

    Francesco Bodria & Fosca Giannotti

  2. University of Pisa, Largo Bruno Pontecorvo, 3, Pisa, Italy

    Riccardo Guidotti & Dino Pedreschi

Authors
  1. Francesco Bodria
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  2. Riccardo Guidotti
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  3. Fosca Giannotti
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  4. Dino Pedreschi
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Corresponding author

Correspondence to Riccardo Guidotti .

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Editors and Affiliations

  1. University of Montpellier, Montpellier, France

    Poncelet Pascal

  2. INRAE, Montpellier, France

    Dino Ienco

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Bodria, F., Guidotti, R., Giannotti, F., Pedreschi, D. (2022). Interpretable Latent Space to Enable Counterfactual Explanations. In: Pascal, P., Ienco, D. (eds) Discovery Science. DS 2022. Lecture Notes in Computer Science(), vol 13601. Springer, Cham. https://doi.org/10.1007/978-3-031-18840-4_37

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  • DOI: https://doi.org/10.1007/978-3-031-18840-4_37

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  • Online ISBN: 978-3-031-18840-4

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