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Explainable Machine Learning via Argumentation

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Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1903))

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Abstract

This paper presents a general Explainable Machine Learning framework and methodology based on Argumentation (ArgEML). The flexible reasoning form of argumentation in the face of unknown and incomplete information together with the direct link of argumentation to justification and explanation enables the development of a natural form of explainable machine learning. In this form of learning the explanations are useful not only for supporting the final predictions but also play a significant role in the learning process itself. The paper defines the basic theoretical notions of ArgEML together with its main machine learning operators and method of application. It describes how such an argumentation-based approach can give a flexible way for learning that recognizes difficult cases (with respect to the current available training data) and separates these cases out not as definite predictive cases but as cases where it is more appropriate to explainably analyze the alternative predictions. Using the argumentation-based explanations we can partition the problem space into groups characterized by the basic argumentative tension between arguments for and against the alternatives. The paper presents a first evaluation of the approach by applying the ArgEML learning methodology both on artificial and on real-life datasets.

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Notes

  1. 1.

    We will use a Logic Programming rule notation for the arguments to facilitate the exposition of the realization of the ArgEML framework in the next sections of the paper.

  2. 2.

    https://www.swi-prolog.org/.

  3. 3.

    https://jpl7.org/.

  4. 4.

    http://gorgiasb.tuc.gr/.

  5. 5.

    https://cran.r-project.org/web/packages/inTrees/index.html.

  6. 6.

    All datasets presented here are available via request from the authors.

  7. 7.

    https://archive.ics.uci.edu/ml/datasets/iris.

  8. 8.

    A measure of each rule’s relative importance in predicting the correct class.

  9. 9.

    st:Stenosis (%ECST). ECST: European Carotid Surgery Trial. Lngsm40: Log(GSM + 40). GSM: Grey Scale Median. Cubrar: (Plaque Area)1/3 in mm2. Dwa1: DWAs (#of Yes cases).DWA: Discrete White Areas. Ctiastr1: History of contr. TIAs and/or Stroke (#of Yes cases). Target: {asympt, stroke}.

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Acknowledgements

Part of this work was undertaken under the University of Cyprus internal project, Integrated Explainable AI (IXAI) for Medical Decision Support, ARGEML 8037P-22046. This study is also partly funded by the project ‘Atherorisk’ “Identification of unstable carotid plaques associated with symptoms using ultrasonic image analysis and plaque motion analysis”, code: Excellence/0421/0292, funded by the Research and In-novation Foundation, the Republic of Cyprus.

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Authors and Affiliations

  1. University of Cyprus, 1 Panepistimiou Avenue, 2109, Nicosia, Cyprus

    Nicoletta Prentzas, Constantinos Pattichis & Antonis Kakas

  2. CYENS Centre of Excellence, 23 Dimarchou Lellou Demetriadi, 1016, Nicosia, Cyprus

    Constantinos Pattichis

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  1. Nicoletta Prentzas
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Correspondence to Nicoletta Prentzas .

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  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

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Prentzas, N., Pattichis, C., Kakas, A. (2023). Explainable Machine Learning via Argumentation. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1903. Springer, Cham. https://doi.org/10.1007/978-3-031-44070-0_19

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