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  • Conference paper
    Potyka N, Yin X, Toni F, 2022,

    Explaining Random Forests using Bipolar Argumentation and Markov Networks

    , Francesca Toni, ISSN: 2159-5399
  • Conference paper
    Jiang J, Leofante F, Rago A, Toni Fet al., 2022,

    Formalising the Robustness of Counterfactual Explanations for Neural Networks

    , The 37th AAAI Conference on Artificial Intelligence
  • Conference paper
    Albini E, Rago A, Baroni P, Toni Fet al., 2022,

    Descriptive accuracy in explanations: the case of probabilistic classifiers

    , 15th International Conference on Scalable Uncertainty Management (SUM 2022)

    A user receiving an explanation for outcomes produced byan artificially intelligent system expects that it satisfies the key propertyof descriptive accuracy (DA), i.e. that the explanation contents are incorrespondence with the internal working of the system. Crucial as thisproperty appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions offormalising DA and of analysing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialecticalDA, using the family of probabilistic classifiers as the context for ouranalysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attributionmethods from the literature and a novel form of explanation that wepropose and complement our analysis with experiments carried out on avaried selection of concrete probabilistic classifiers.

  • Conference paper
    Maurizio P, Toni F, 2022,

    Learning assumption-based argumentation frameworks

    , 31st International Conference on Inductive Logic Programming (ILP 2022)

    . We propose a novel approach to logic-based learning whichgenerates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. TheseABA frameworks can be mapped onto logic programs with negationas failure that may be non-stratified. Whereas existing argumentationbased methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformationrules, including some adapted from logic program transformation rules(notably folding) as well as others, such as rote learning and assumptionintroduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we alsopropose a variant that handles the non-stratified case. We illustrate thebenefits of our approach with a number of examples, which show that,on one hand, we are able to easily reconstruct other logic-based learningapproaches and, on the other hand, we can work out in a very simpleand natural way problems that seem to be hard for existing techniques.

  • Conference paper
    Jiang J, Rago A, Toni F, 2022,

    Should counterfactual explanations always be data instances?

    , XLoKR 2022: The Third Workshop on Explainable Logic-Based Knowledge Representation

    Counterfactual explanations (CEs) are an increasingly popular way of explaining machine learning classifiers. Predominantly, they amount to data instances pointing to potential changes to the inputs that would lead to alternative outputs. In this position paper we question the widespread assumption that CEs should always be data instances, and argue instead that in some cases they may be better understood in terms of special types of relations between input features and classification variables. We illustrate how a special type of these relations, amounting to critical influences, can characterise and guide the search for data instances deemed suitable as CEs. These relations also provide compact indications of which input features - rather than their specific values in data instances - have counterfactual value.

  • Conference paper
    Rago A, Baroni P, Toni F, 2022,

    Explaining causal models with argumentation: the case of bi-variate reinforcement

    , 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022), Publisher: IJCAI Organisation, ISSN: 2334-1033

    Causal models are playing an increasingly important role inmachine learning, particularly in the realm of explainable AI.We introduce a conceptualisation for generating argumenta-tion frameworks (AFs) from causal models for the purposeof forging explanations for the models’ outputs. The concep-tualisation is based on reinterpreting desirable properties ofsemantics of AFs as explanation moulds, which are meansfor characterising the relations in the causal model argumen-tatively. We demonstrate our methodology by reinterpretingthe property of bi-variate reinforcement as an explanationmould to forge bipolar AFs as explanations for the outputs ofcausal models. We perform a theoretical evaluation of theseargumentative explanations, examining whether they satisfy arange of desirable explanatory and argumentative propertie

  • Conference paper
    Potyka N, Yin X, Toni F, 2022,

    On the Tradeoff Between Correctness and Completeness in Argumentative Explainable AI

    , ISSN: 1613-0073

    Explainable AI aims at making the decisions of autonomous systems human-understandable. Argumentation frameworks are a natural tool for this purpose. Among them, bipolar abstract argumentation frameworks seem well suited to explain the effect of features on a classification decision and their formal properties can potentially be used to derive formal guarantees for explanations. Two particular interesting properties are correctness (if the explanation says that X affects Y, then X affects Y ) and completeness (if X affects Y, then the explanation says that X affects Y ). The reinforcement property of bipolar argumentation frameworks has been used as a natural correctness counterpart in previous work. Applied to the classification context, it basically states that attacking features should decrease and supporting features should increase the confidence of a classifier. In this short discussion paper, we revisit this idea, discuss potential limitations when considering reinforcement without a corresponding completeness property and how these limitations can potentially be overcome.

  • Conference paper
    Sukpanichnant P, Rago A, Lertvittayakumjorn P, Toni Fet al., 2021,

    LRP-based argumentative explanations for neural networks

    , 2021 - Italian Workshop on Explainable Artificial Intelligence, Pages: 71-84, ISSN: 1613-0073

    In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.

  • Conference paper
    Rago A, Russo F, Albini E, Baroni P, Toni Fet al., 2021,

    Forging Argumentative Explanations from Causal Models

    , ISSN: 1613-0073

    We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for models' outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the extracted bipolar AFs may be used as relation-based explanations for the outputs of causal models.

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