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Reasoning Web International Summer School

Reasoning Web 2020: Reasoning Web. Declarative Artificial Intelligence pp 53–84Cite as

Explanation via Machine Arguing

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12258))

Abstract

As AI becomes ever more ubiquitous in our everyday lives, its ability to explain to and interact with humans is evolving into a critical research area. Explainable AI (XAI) has therefore emerged as a popular topic but its research landscape is currently very fragmented. Explanations in the literature have generally been aimed at addressing individual challenges and are often ad-hoc, tailored to specific AIs and/or narrow settings. Further, the extraction of explanations is no simple task; the design of the explanations must be fit for purpose, with considerations including, but not limited to: Is the model or a result being explained? Is the explanation suited to skilled or unskilled explainees? By which means is the information best exhibited? How may users interact with the explanation? As these considerations rise in number, it quickly becomes clear that a systematic way to obtain a variety of explanations for a variety of users and interactions is much needed. In this tutorial we will overview recent approaches showing how these challenges can be addressed by utilising forms of machine arguing as the scaffolding underpinning explanations that are delivered to users. Machine arguing amounts to the deployment of methods from computational argumentation in AI with suitably mined argumentation frameworks, which provide abstractions of “debates”. Computational argumentation has been widely used to support applications requiring information exchange between AI systems and users , facilitated by the fact that the capability of arguing is pervasive in human affairs and arguing is core to a multitude of human activities: humans argue to explain, interact and exchange information. Our lecture will focus on how machine arguing can serve as the driving force of explanations in AI in different ways, namely: by building explainable systems with argumentative foundations from linguistic data focusing on reviews), or by extracting argumentative reasoning from existing systems (focusing on a recommender system) .

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Notes

  1. 1.

    These alternative notations are used interchangeably in the literature, as we do here.

  2. 2.

    Note that several other notions could be used, as overviewed in [7]. we have chosen this specific notion because it satisfies some desirable properties [7] as well as performing well in practice [11].

  3. 3.

    Note that this recursively defined notion treats strengths of attackers and supporters as sets, but needs to consider them in sequence (thus the mention of ‘an arbitrary permutation’).

  4. 4.

    https://www.rottentomatoes.com.

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Authors and Affiliations

  1. Imperial College London, London, UK

    Oana Cocarascu, Antonio Rago & Francesca Toni

Authors
  1. Oana Cocarascu
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  2. Antonio Rago
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  3. Francesca Toni
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Correspondence to Francesca Toni .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Marco Manna

  2. School of Informatics, University of Edinburgh, Edinburgh, UK

    Andreas Pieris

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Cocarascu, O., Rago, A., Toni, F. (2020). Explanation via Machine Arguing. In: Manna, M., Pieris, A. (eds) Reasoning Web. Declarative Artificial Intelligence. Reasoning Web 2020. Lecture Notes in Computer Science(), vol 12258. Springer, Cham. https://doi.org/10.1007/978-3-030-60067-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-60067-9_3

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