A method for explaining Bayesian networks for legal evidence with scenarios

Vlek, Charles; Prakken, Henry; Renooij, Silja; Verheij, Bart

doi:10.1007/s10506-016-9183-4

Cited by 58 publications

(50 citation statements)

References 19 publications

(46 reference statements)

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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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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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“…In the literature, scenario schemes have been used to represent a scenario's completeness (Bex 2011;Bex and Verheij 2013;Verheij et al 2016;Vlek et al 2014Vlek et al , 2016, taking inspiration from the use of scripts in artificial intelligence and cognitive science (Schank and Abelson 1977). Here the cases in a case model are used to represent completeness.…”

Section: Scenariosmentioning

confidence: 99%

“…• A method to generate a structured explanatory text of a Bayesian network modeled according to this method (Vlek 2016;Vlek et al 2016); • An algorithm to extract argumentative information from a Bayesian network modeling hypotheses and evidence ); • A method to incorporate argument schemes in a Bayesian network Timmer et al 2015a). …”

Section: Introductionmentioning

confidence: 99%

Proof with and without probabilities

Verheij

2017

Artif Intell Law

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Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock's film 'To Catch A Thief'.

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“…Indeed, non-Bayesian approaches to legal argumentation have tended to be narrative-based with a focus on comparisons between competing stories and explanations with much less emphasis on formal integration [2], [3], [4], [9]. Recent work that incorporates scenario-based approaches with Bayesian networks has attempted to partly address this problem [10], [11]. Likewise, a convincing attempt at integrating narrative and probabilistic perspectives has been presented in [12], with an emphasis on modelling more than mere evidence but also considering competing narratives, explanations and notions of credibility and resiliency.…”

Section: Introductionmentioning

confidence: 99%

Modelling competing legal arguments using Bayesian model comparison and averaging

et al. 2019

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Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and 'average' Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational.

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A method for explaining Bayesian networks for legal evidence with scenarios

Cited by 58 publications

References 19 publications

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

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

Proof with and without probabilities

Modelling competing legal arguments using Bayesian model comparison and averaging

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