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  • Conference paper
    Zhang K, Toni F, Williams M, 2023,

    A federated cox model with non-proportional hazards

    , The 6th International Workshop on ​Health Intelligence, Publisher: Springer, ISSN: 1860-949X

    Recent research has shown the potential for neural networksto improve upon classical survival models such as the Cox model, whichis widely used in clinical practice. Neural networks, however, typicallyrely on data that are centrally available, whereas healthcare data arefrequently held in secure silos. We present a federated Cox model thataccommodates this data setting and also relaxes the proportional hazardsassumption, allowing time-varying covariate effects. In this latter respect,our model does not require explicit specification of the time-varying ef-fects, reducing upfront organisational costs compared to previous works.We experiment with publicly available clinical datasets and demonstratethat the federated model is able to perform as well as a standard model.

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

    Explaining random forests using bipolar argumentation and Markov networks

    , AAAI 23, Publisher: AAAI, ISSN: 2159-5399

    Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. We show that their decision process can be naturally represented as an argumentation problem, which allows creating global explanations via argumentative reasoning. We generalize sufficientand necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the explanation problems is high, we present an efficient approximation algorithm with probabilistic approximation guarantees.

  • 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, Publisher: Association for the Advancement of Artificial Intelligence

    The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. However, recent studies have shown that these explanations may not be robust to changes in the underlying model (e.g., following retraining), which raises questions about their reliability in real-world applications. Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees. To remedy this, we propose the first notion to formally and deterministically assess the robustness (to model changes) of CFXs for neural networks, that we call ∆-robustness. We introduce an abstraction framework based on interval neural networks to verify the ∆-robustness of CFXs against a possibly infinite set of changes to the model parameters, i.e., weights and biases. We then demonstrate the utility of this approach in two distinct ways. First, we analyse the ∆-robustness of a number of CFX generation methods from the literature and show that they unanimously host significant deficiencies in this regard. Second, we demonstrate how embedding ∆-robustness within existing methods can provide CFXs which are provably robust.

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

    On the tradeoff between correctness and completeness in argumentative explainable AI

    , 1st International Workshop on Argumentation for eXplainable AI, Publisher: CEUR Workshop Proceedings, Pages: 1-8, 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., 2022,

    Neural QBAFs: explaining neural networks under LRP-based argumentation frameworks

    , International Conference of the Italian Association for Artificial Intelligence, Publisher: Springer International Publishing, Pages: 429-444, ISSN: 0302-9743

    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
    Ward F, Belardinelli F, Toni F, 2022,

    Argumentative Reward Learning: Reasoning About Human Preferences

    , HMCaT 2022 (ICML)
  • 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
    Ward F, Belardinelli F, Toni F, 2022,

    Argumentative Reward Learning: Reasoning About Human Preferences

    , MPREF 2022 (IJCAI-ECAI 2022)
  • Conference paper
    Ward F, Toni F, Belardinelli F, 2022,

    A Casual Perspective on AI Deception

    , CAUSAL 22 (ICLP)
  • Conference paper
    Ward F, Toni F, Belardinelli F, 2022,

    A Causal Perspective on AI Deception in Games

    , AI Safety 2022 (IJCAI-ECAI-22)
  • Conference paper
    Irwin B, Rago A, Toni F, 2022,

    Argumentative forecasting

    , AAMAS 2022, Publisher: ACM, Pages: 1636-1638

    We introduce the Forecasting Argumentation Framework (FAF), anovel argumentation framework for forecasting informed by re-cent judgmental forecasting research. FAFs comprise update frame-works which empower (human or artificial) agents to argue overtime with and about probability of scenarios, whilst flagging per-ceived irrationality in their behaviour with a view to improvingtheir forecasting accuracy. FAFs include three argument types withfuture forecasts and aggregate the strength of these arguments toinform estimates of the likelihood of scenarios. We describe animplementation of FAFs for supporting forecasting agents.

  • Conference paper
    Gaskell A, Miao Y, Toni F, Specia Let al., 2022,

    Logically consistent adversarial attacks for soft theorem provers

    , 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence, Publisher: International Joint Conferences on Artificial Intelligence

    Recent efforts within the AI community haveyielded impressive results towards “soft theoremproving” over natural language sentences using lan-guage models. We propose a novel, generativeadversarial framework for probing and improvingthese models’ reasoning capabilities. Adversarialattacks in this domain suffer from the logical in-consistency problem, whereby perturbations to theinput may alter the label. Our Logically consis-tent AdVersarial Attacker, LAVA, addresses this bycombining a structured generative process with asymbolic solver, guaranteeing logical consistency.Our framework successfully generates adversarialattacks and identifies global weaknesses commonacross multiple target models. Our analyses revealnaive heuristics and vulnerabilities in these mod-els’ reasoning capabilities, exposing an incompletegrasp of logical deduction under logic programs.Finally, in addition to effective probing of thesemodels, we show that training on the generatedsamples improves the target model’s performance.

  • Conference paper
    Irwin B, Rago A, Toni F, 2022,

    Forecasting argumentation frameworks

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

    We introduce Forecasting Argumentation Frameworks(FAFs), a novel argumentation-based methodology forforecasting informed by recent judgmental forecastingresearch. FAFs comprise update frameworks which empower(human or artificial) agents to argue over time about theprobability of outcomes, e.g. the winner of a politicalelection or a fluctuation in inflation rates, whilst flaggingperceived irrationality in the agents’ behaviour with a viewto improving their forecasting accuracy. FAFs include fiveargument types, amounting to standard pro/con arguments,as in bipolar argumentation, as well as novel proposalarguments and increase/decrease amendment arguments. Weadapt an existing gradual semantics for bipolar argumen-tation to determine the aggregated dialectical strength ofproposal arguments and define irrational behaviour. We thengive a simple aggregation function which produces a finalgroup forecast from rational agents’ individual forecasts.We identify and study properties of FAFs and conductan empirical evaluation which signals FAFs’ potential toincrease the forecasting accuracy of participants.

  • 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
    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
    Albini E, Rago A, Baroni P, Toni Fet al., 2021,

    Influence-driven explanations for bayesian network classifiers

    , PRICAI 2021, Publisher: Springer Verlag, Pages: 88-100, ISSN: 0302-9743

    We propose a novel approach to buildinginfluence-driven ex-planations(IDXs) for (discrete) Bayesian network classifiers (BCs). IDXsfeature two main advantages wrt other commonly adopted explanationmethods. First, IDXs may be generated using the (causal) influences between intermediate, in addition to merely input and output, variables within BCs, thus providing adeep, rather than shallow, account of theBCs’ behaviour. Second, IDXs are generated according to a configurable set of properties, specifying which influences between variables count to-wards explanations. Our approach is thusflexible and can be tailored to the requirements of particular contexts or users. Leveraging on this flexibility, we propose novel IDX instances as well as IDX instances cap-turing existing approaches. We demonstrate IDXs’ capability to explainvarious forms of BCs, and assess the advantages of our proposed IDX instances with both theoretical and empirical analyses.

  • Conference paper
    Rago A, Cocarascu O, Bechlivanidis C, Toni Fet al., 2020,

    Argumentation as a framework for interactive explanations for recommendations

    , KR 2020, 17th International Conference on Principles of Knowledge Representation and Reasoning, Publisher: IJCAI, Pages: 805-815, ISSN: 2334-1033

    As AI systems become ever more intertwined in our personallives, the way in which they explain themselves to and inter-act with humans is an increasingly critical research area. Theexplanation of recommendations is, thus a pivotal function-ality in a user’s experience of a recommender system (RS),providing the possibility of enhancing many of its desirablefeatures in addition to itseffectiveness(accuracy wrt users’preferences). For an RS that we prove empirically is effective,we show how argumentative abstractions underpinning rec-ommendations can provide the structural scaffolding for (dif-ferent types of) interactive explanations (IEs), i.e. explana-tions empowering interactions with users. We prove formallythat these IEs empower feedback mechanisms that guaranteethat recommendations will improve with time, hence render-ing the RSscrutable. Finally, we prove experimentally thatthe various forms of IE (tabular, textual and conversational)inducetrustin the recommendations and provide a high de-gree oftransparencyin the RS’s functionality.

  • Conference paper
    Kotonya N, Spooner T, Magazzeni D, Toni Fet al., 2021,

    Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification

    , FEVER 2021
  • Book chapter
    Cocarascu O, Cyras K, Rago A, Toni Fet al., 2021,

    Mining property-driven graphical explanations for data-centric AI from argumentation frameworks

    , Human-Like Machine Intelligence, Pages: 93-113, ISBN: 9780198862536
  • Conference paper
    Cyras K, Rago A, Emanuele A, Baroni P, Toni Fet al., 2021,

    Argumentative XAI: a survey

    , The 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Publisher: International Joint Conferences on Artificial Intelligence, Pages: 4392-4399

    Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentative models have been advocated in both the AI and social science literature, as their dialectical nature appears to match some basic desirable features of the explanation activity. In this survey we overview XAI approaches built using methods from the field of computational argumentation, leveraging its wide array of reasoning abstractions and explanation delivery methods. We overview the literature focusing on different types of explanation (intrinsic and post-hoc), different models with which argumentation-based explanations are deployed, different forms of delivery, and different argumentation frameworks they use. We also lay out a roadmap for future work.

  • Conference paper
    Zylberajch H, Lertvittayakumjorn P, Toni F, 2021,

    HILDIF: interactive debugging of NLI models using influence functions

    , 1st Workshop on Interactive Learning for Natural Language Processing (InterNLP), Publisher: ASSOC COMPUTATIONAL LINGUISTICS-ACL, Pages: 1-6

    Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability. One solution to this problem is to include users in the loop and leverage their feedback to improve models. We propose a novel explanatory debugging pipeline called HILDIF, enabling humans to improve deep text classifiers using influence functions as an explanation method. We experiment on the Natural Language Inference (NLI) task, showing that HILDIF can effectively alleviate artifact problems in fine-tuned BERT models and result in increased model generalizability.

  • Journal article
    Albini E, Baroni P, Rago A, Toni Fet al., 2021,

    Interpreting and explaining pagerank through argumentation semantics

    , Intelligenza Artificiale, Vol: 15, Pages: 17-34, ISSN: 1724-8035

    In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.

  • Report
    Paulino-Passos G, Toni F, 2021,

    Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)

    Recently, abstract argumentation-based models of case-based reasoning($AA{\text -} CBR$ in short) have been proposed, originally inspired by thelegal domain, but also applicable as classifiers in different scenarios.However, the formal properties of $AA{\text -} CBR$ as a reasoning systemremain largely unexplored. In this paper, we focus on analysing thenon-monotonicity properties of a regular version of $AA{\text -} CBR$ (that wecall $AA{\text -} CBR_{\succeq}$). Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously monotonic, a property frequently considereddesirable in the literature. We then define a variation of $AA{\text -}CBR_{\succeq}$ which is cautiously monotonic. Further, we prove that suchvariation is equivalent to using $AA{\text -} CBR_{\succeq}$ with a restrictedcasebase consisting of all "surprising" and "sufficient" cases in the originalcasebase. As a by-product, we prove that this variation of $AA{\text -}CBR_{\succeq}$ is cumulative, rationally monotonic, and empowers a principledtreatment of noise in "incoherent" casebases. Finally, we illustrate $AA{\text-} CBR$ and cautious monotonicity questions on a case study on the U.S. TradeSecrets domain, a legal casebase.

  • Journal article
    Rago A, Cocarascu O, Bechlivanidis C, Lagnado D, Toni Fet al., 2021,

    Argumentative explanations for interactive recommendations

    , Artificial Intelligence, Vol: 296, Pages: 1-22, ISSN: 0004-3702

    A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.

  • Journal article
    Cyras K, Oliveira T, Karamlou M, Toni Fet al., 2021,

    Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

    , Argument and Computation, Vol: 12, Pages: 149-189, ISSN: 1946-2166

    A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realis

  • Conference paper
    Dejl A, He P, Mangal P, Mohsin H, Surdu B, Voinea E, Albini E, Lertvittayakumjorn P, Rago A, Toni Fet al., 2021,

    Argflow: a toolkit for deep argumentative explanations for neural networks.

    , Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems, Publisher: ACM, Pages: 1761-1763

    In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, theyare often complex and incomprehensible to human users. This candecrease trust in their outputs and render their usage in criticalsettings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particularfor black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain outputs in termsof inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling thegeneration of a variety of ‘deep’ argumentative explanations (DAXs)for outputs of NNs on classification tasks.

  • Journal article
    Cyras K, Heinrich Q, Toni F, 2021,

    Computational complexity of flat and generic assumption-based argumentation, with and without probabilities

    , Artificial Intelligence, Vol: 293, Pages: 1-36, ISSN: 0004-3702

    Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA, but not to PSA. We study the complexity of an existing form of PSA, namely Probabilistic Assumption-Based Argumentation (PABA), a powerful, implemented formalism which subsumes several forms of PAA and other forms of PSA. Specifically, we establish membership (general upper bounds) and completeness (instantiated lower bounds) of reasoning in PABA for the class FP#P (of functions with a #P-oracle for counting the solutions of an NP problem) with respect to newly introduced probabilistic verification, credulous and sceptical acceptance function problems under several ABA semantics. As a by-product necessary to establish PABA complexity results, we provide a comprehensive picture of the ABA complexity landscape (for both flat and generic, possibly non-flat ABA) for the classical decision problems of verification, existence, credulous and sceptical acceptance under those ABA semantics.

  • 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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