An explainable multi-attribute decision model based on argumentation

Zhong, Qiaoting; Fan, Xiangyu; Luo, Xudong; Toni, Francesca

doi:10.1016/j.eswa.2018.09.038

Cited by 56 publications

(31 citation statements)

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“…Argumentation is indeed well-suited for explainable reasoning [6,40,70] with argumentative explanations proposed in various settings, see e.g. [1][2][3]5,14,16,18,[21][22][23][24]26,28,31,32,[37][38][39]42,43,51,57,58,62,64,66,69,70,77,79,81,[84][85][86][87][88]90,93,101,103,104,[110][111][112]. We hope to exploit the well-established as well as novel ABA + mechanisms to our advantage of providing various explanations to accompany the decisions supported by ABA + G. In addition to several other future work directions mentioned in Sections 6 and 7, we will aim…”

Section: Discussionmentioning

confidence: 99%

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

Čyras

Oliveira

Karamlou

et al. 2021

AAC

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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 realistic personalised clinical reasoning scenaria.

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Section: Discussionmentioning

confidence: 99%

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

Čyras

Oliveira

Karamlou

et al. 2021

AAC

Self Cite

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“…In Zhong et al . (2014), the authors define an AF that takes into consideration information from the KB to make a decision using similar decisions. The framework first parses the text of the argument and extracts the most important features (nouns, verbs).…”

Section: Argumentation and Explainabilitymentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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“…Another approach for explaining the reasoning in BNs is based on argumentation theory, widely used in legal cases. Arguments are extracting from a BN though an argument 'support graph' [18], [19], [20], [21] from which an argument graph is extracted depending on the evidence. Although this representation has a history of application to the law, as far as we are aware it has not been widely applied in medicine.…”

Section: Related Work On Explanation Of Reasoning In a Bnmentioning

confidence: 99%

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making

Kyrimi

Mossadegh

Tai

et al. 2020

Artificial Intelligence in Medicine

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Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to 'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted. This paper considers Bayesian Networks (BNs): directed acyclic graphs, showing causal or influential relationships between random variables. These variables can be discrete or continuous, and a BN with both is called `hybrid'. The uncertain relationships between connected variables are expressed using conditional probabilities. The strength of these relationships is captured in the Node Probability Table (NPT), used to represent the conditional probability distribution of each node in the BN given its parents. Once values for all NPTs are given, the BN is fully parameterized, and probabilistic reasoning (using Bayesian inference) can be performed. However, the reasoning process is not always easy for a user to follow [6], [7], [8].In contrast to many CDS models, a BN is not a black box and its reasoning can be explained [6], [9]. Several approaches have been proposed to explain the reasoning of a BN (presented in Section 3). However, there are many situations where these methods cannot be applied. First, most of the methods are applied to BNs that include only discrete variables. Some of them are even restricted to binary variables only. However, most of the medical BNs include continuous nodes as well. In addition, most of them try to find the best explanation that can be time-consuming, especially for large BNs, which are common in medical applications. Finally, in some methods, user input is required in different stages of the explanation. This can be problematic, especially in situations where there is time pressure.In this paper, we propose a practical method of explaining the reasoning in a BN, so that a user can understand how a prediction is generated. The method is an extension of a previous conference paper published by the authors [10]. Our proposed method can be used in hybrid networks that have both continuous and discrete nodes and requires no user input. In addition, we simplify the process of identifying the most important evidence and chains of reasoning, so we can rapidly produce a good and concise explanation, but not necessarily the most complete one. In fact, our method produces an incremental explanation that has three successive l...

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An explainable multi-attribute decision model based on argumentation

Cited by 56 publications

References 56 publications

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

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

Argumentation and explainable artificial intelligence: a survey

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making

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