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Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI

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Explainable Artificial Intelligence (xAI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2153))

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Abstract

We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustworthy, similar inputs should yield similar explanations, reflecting a consistent semantic understanding. By leveraging XAI techniques, we assess semantic continuity in the task of image recognition. We conduct experiments to observe how incremental changes in input affect the explanations provided by different XAI methods. Through this approach, we aim to evaluate the models’ capability to generalize and abstract semantic concepts accurately and to evaluate different XAI methods in correctly capturing the model behaviour. This paper contributes to the broader discourse on AI interpretability by proposing a quantitative measure for semantic continuity for XAI methods, offering insights into the models’ and explainers’ internal reasoning processes, and promoting more reliable and transparent AI systems.

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Author information

Authors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Qi Huang & Niki van Stein

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Emanuele Mezzi

  3. Wageningen Food Safety Research, Wageningen, The Netherlands

    Osman Mutlu

  4. University of Amsterdam, Amsterdam, The Netherlands

    Miltiadis Kofinas

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Vidya Prasad

  6. Sorbonne University, Paris, France

    Shadnan Azwad Khan

  7. Netherlands eScience center, Amsterdam, The Netherlands

    Elena Ranguelova

Authors
  1. Qi Huang
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  2. Emanuele Mezzi
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  3. Osman Mutlu
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  4. Miltiadis Kofinas
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  5. Vidya Prasad
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  6. Shadnan Azwad Khan
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  7. Elena Ranguelova
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  8. Niki van Stein
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Correspondence to Niki van Stein .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI, Berlin, Germany

    Sebastian Lapuschkin

  3. University of Marburg, Marburg, Germany

    Christin Seifert

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Huang, Q. et al. (2024). Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2153. Springer, Cham. https://doi.org/10.1007/978-3-031-63787-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-63787-2_16

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