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Journal articleKampik T, Potyka N, Yin X, et al., 2024,
Contribution functions for quantitative bipolar argumentation graphs: a principle-based analysis
, International Journal of Approximate Reasoning, Vol: 173, ISSN: 0888-613XWe present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introduced principles formalise the intuitions underlying different contribution functions as well as expectations one would have regarding the behaviour of contribution functions in general. As none of the covered contribution functions satisfies all principles, our analysis can serve as a tool that enables the selection of the most suitable function based on the requirements of a given use case.
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Conference paperRusso F, Rapberger A, Toni F, 2024,
Argumentative Causal Discovery
, The 21st International Conference on Knowledge Representation and Reasoning (KR-2024) -
Conference paperLeofante F, Ayoobi H, Dejl A, et al., 2024,
Contestable AI needs Computational Argumentation
, The 21st International Conference on Knowledge Representation and Reasoning (KR-2024) -
Conference paperYin X, Potyka N, Toni F, 2024,
CE-QArg: Counterfactual explanations for quantitative bipolar argumentation frameworks
, 21st International Conference on Principles of Knowledge Representation and Reasoning, Publisher: International Joint Conferences on Artificial Intelligence OrganizationThere is a growing interest in understanding arguments’strength in Quantitative Bipolar Argumentation Frameworks(QBAFs). Most existing studies focus on attribution-basedmethods that explain an argument’s strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority, which help determine the updating direction and magnitude for each argument, respectively. We discuss some formal properties of our counterfactual explanations and empirically evaluate CE-QArg on randomly generated QBAFs.
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Conference paperGould A, Paulino Passos G, Dadhania S, et al., 2024,
Preference-Based Abstract Argumentation for Case-Based Reasoning
, International Conference on Principles of Knowledge Representation and ReasoningIn the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.
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Conference paperFreedman G, Toni F, 2024,
Detecting Scientific Fraud Using Argument Mining
, ArgMining@ACL2024, Publisher: The MIT Press -
Conference paperLehtonen T, Rapberger A, Toni F, et al., 2024,
On Computing Admissibility in ABA
, 10th International Conference on Computational Models of Argument (COMMA 2024) -
Conference paperRapberger A, Toni F, 2024,
On the Robustness of Argumentative Explanations
, 10th International Conference on Computational Models of Argument (COMMA 2024) -
Conference paperJiang J, Leofante F, Rago A, et al., 2024,
Robust Counterfactual Explanations in Machine Learning: A Survey
, The 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 -
Conference paperYin X, Potyka N, Toni F, 2024,
Explaining arguments’ strength: unveiling the role of attacks and supports
, IJCAI 2024, Publisher: IJCAI -
Conference paperUlbricht M, Potyka N, Rapberger A, et al., 2024,
Non-flat ABA is an instance of bipolar argumentation
, The 38th Annual AAAI Conference on Artificial Intelligence, Publisher: AAAI, Pages: 10723-10731, ISSN: 2374-3468Assumption-based Argumentation (ABA) is a well-knownstructured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries. A common restriction im-posed on ABA frameworks (ABAFs) is that they are flat, i.e.,each of the defeasible assumptions can only be assumed, but not derived. While it is known that flat ABAFs can be translated into abstract argumentation frameworks (AFs) as pro-posed by Dung, no translation exists from general, possibly non-flat ABAFs into any kind of abstract argumentation formalism. In this paper, we close this gap and show that bipolar AFs (BAFs) can instantiate general ABAFs. To this end we develop suitable, novel BAF semantics which borrow from the notion of deductive support. We investigate basic properties of our BAFs, including computational complexity, and prove the desired relation to ABAFs under several semantics.
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Conference paperZhang D, Williams M, Toni F, 2024,
Targeted activation penalties help CNNs ignore spurious signals
, The 38th Annual AAAI Conference on Artificial Intelligence, Publisher: AAAI, ISSN: 2159-5399Neural networks (NNs) can learn to rely on spurious signals in the training data, leading to poor generalisation. Recent methods tackle this problem by training NNs with additional ground-truth annotations of such signals. These methods may, however, let spurious signals re-emerge in deep convolutional NNs (CNNs). We propose Targeted Activation Penalty (TAP), a new method tackling the same problem by penalising activations to control the re-emergence of spurious signals in deep CNNs, while also lowering training times and memory usage. In addition, ground-truth annotations can be expensive to obtain. We show that TAP still works well with annotations generated by pre-trained models as effective substitutes of ground-truth annotations. We demonstrate the power of TAP against two state-of-the-art baselines on the MNIST benchmark and on two clinical image datasets, using four different CNN architectures.
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Conference paperJiang J, Rago A, Leofante F, et al., 2024,
Recourse under model multiplicity via argumentative ensembling
, The 23rd International Conference on Autonomous Agents and Multi-Agent Systems, Publisher: ACMModel Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task. Recent studies show that models obtained under MM may produce inconsistent predictions for the same input. When this occurs, it becomes challenging to provide counterfactual explanations(CEs), a common means for offering recourse recommendations to individuals negatively affected by models’ predictions. In this paper, we formalise this problem, which we name recourse-aware ensembling, and identify several desirable properties which methods for solving it should satisfy. We demonstrate that existing ensemblingmethods, naturally extended in different ways to provide CEs, fail to satisfy these properties. We then introduce argumentative ensembling, deploying computational argumentation as a means to guarantee robustness of CEs to MM, while also accommodating customisable user preferences. We show theoretically and experimentally that argumentative ensembling is able to satisfy propertieswhich the existing methods lack, and that the trade-offs are minimal wrt the ensemble’s accuracy.
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Conference paperJiang J, Lan J, Leofante F, et al., 2023,
Provably robust and plausible counterfactual explanations for neural networks via robust optimisation
, The 15th Asian Conference on Machine Learning, Publisher: ML Research PressCounterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are classified with a different label than the output. To tackle the established problem that CEs are easily invalidated when model parameters are updated (e.g. retrained), studies have proposed ways to certify the robustness of CEs under model parameter changes bounded by a norm ball. However, existing methods targeting this form of robustness are not sound or complete, and they may generate implausible CEs, i.e., outliers wrt the training dataset. In fact, no existing method simultaneously optimises for closeness and plausibility while preserving robustness guarantees. In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature. We formulate an iterative algorithm to compute provably robust CEs and prove its convergence, soundness and completeness. Through a comparative experiment involving six baselines, five of which target robustness, we show that PROPLACE achieves state-of-the-art performances against metrics on three evaluation aspects.
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Conference paperTirsi C-G, Proietti M, Toni F, 2023,
ABALearn: an automated logic-based learning system for ABA frameworks
, AIxIA 2023, Publisher: Springer Nature, ISSN: 1687-7470We introduce ABALearn, an automated algorithm that learns Assumption-Based Argumentation (ABA) frameworks from training data consisting of positive and negative examples, and a given background knowledge. ABALearn’s ability to generate comprehensible rules for decision-making promotes transparency and interpretability, addressing the challenges associated with the black-box nature of traditional machine learning models. This implementation is based on the strategy proposed in a previous work. The resulting ABA frameworks can be mapped onto logicprograms with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrat-ing its potential to be used in real-world applications. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) andprovides insights into how such learners can be applied to make predictions.
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Conference paperPaulino Passos G, Toni F, 2023,
Learning Case Relevance in Case-Based Reasoning with Abstract Argumentation
, The 36th International Conference on Legal Knowledge and Information Systems -
Conference paperRusso F, Toni F, 2023,
Causal discovery and knowledge injection for contestable neural networks
, 26th European Conference on Artificial Intelligence ECAI 2023, Publisher: IOS Press, Pages: 2025-2032, ISSN: 0922-6389Neural networks have proven to be effective at solvingmachine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novelmethod overcoming these issues by allowing a two-way interactionwhereby neural-network-empowered machines can expose the underpinning learnt causal graphs and humans can contest the machinesby modifying the causal graphs before re-injecting them into the machines, so that the learnt models are guaranteed to conform to thegraphs and adhere to expert knowledge (some of which can also begiven up-front). By building a window into the model behaviour andenabling knowledge injection, our method allows practitioners to debug networks based on the causal structure discovered from the dataand underpinning the predictions. Experiments with real and synthetic tabular data show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7xsmaller in the input layer, compared to SOTA regularised networks.
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Conference paperYin X, Potyka N, Toni F, 2023,
Argument attribution explanations in quantitative bipolar argumentation frameworks
, 26th European Conference on Artificial Intelligence ECAI 2023, Publisher: IOS Press, Pages: 2898-2905, ISSN: 0922-6389Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.
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Conference paperRago A, Li H, Toni F, 2023,
Interactive explanations by conflict resolution via argumentative exchanges
, 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023), Publisher: IJCAI Organization, Pages: 582-592, ISSN: 2334-1033As the field of explainable AI (XAI) is maturing, calls forinteractive explanations for (the outputs of) AI models aregrowing, but the state-of-the-art predominantly focuses onstatic explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computational argumentation. Specifically, we define Argumentative eXchanges (AXs) for dynamically sharing, in multi-agent systems, information harboured in individual agents’ quantitative bipolar argumentation frameworks towards resolving conflicts amongst the agents. We then deploy AXs in the XAI setting in which a machine and a human interact about the machine’s predictions. We identify and assess several theoretical properties characterising AXs that are suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent behaviours, e.g. capturing counterfactual patterns of reasoning in machines and highlighting the effects ofcognitive biases in humans. We show experimentally (in asimulated environment) the comparative advantages of these behaviours in terms of conflict resolution, and show that the strongest argument may not always be the most effective.
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Conference paperRago A, Gorur D, Toni F, 2023,
ArguCast: a system for online multi-forecasting with gradual argumentation
, Knowledge Representation 2023, Publisher: CEUR-WS.org, Pages: 40-51Judgmental forecasting is a form of forecasting which employs (human) users to make predictions about specied future events. Judgmental forecasting has been shown to perform better than quantitative methods for forecasting, e.g. when historical data is unavailable or causal reasoning is needed. However, it has a number of limitations, arising from users’ irrationality and cognitive biases. To mitigate against these phenomena, we leverage on computational argumentation, a eld which excels in the representation and resolution of conicting knowledge and human-like reasoning, and propose novel ArguCast frameworks (ACFs) and the novel online system ArguCast, integrating ACFs. ACFs and ArguCast accommodate multi-forecasting, by allowing multiple users to debate on multiple forecasting predictions simultaneously, each potentially admitting multiple outcomes. Finally, we propose a novel notion of user rationality in ACFs based on votes on arguments in ACFs, allowing the ltering out of irrational opinions before obtaining group forecasting predictions by means commonly used in judgmental forecasting.
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