A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature

A two-phase method for extracting explanatory arguments from Bayesian networks

Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is to use Bayesian networks (BNs), which are well-known scientific models for probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated, they may appear as black box models. Argumentation models, on the contrary, can be used to show how certain results are derived in a way that naturally corresponds to everyday reasoning. In this paper we propose to explain the inner workings of a BN in terms of arguments. We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network

A method for explaining Bayesian networks for legal evidence with scenarios

In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study

Extracting legal arguments from forensic Bayesian networks

Recent developments in the forensic sciences have confronted the field of legal reasoning with the new challenge of reasoning under uncertainty. Forensic results come with uncertainty and are described in terms of likelihood ratios and random match probabilities. The legal field is unfamiliar with numerical valuations of evidence, which has led to confusion and in some cases to serious miscarriages of justice. The cases of Lucia de B. in the Netherlands and Sally Clark in the UK are infamous examples where probabilistic reasoning has gone wrong with dramatic consequences. One way of structuring probabilistic information is in Bayesian networks(BNs). In this paper we explore a new method to identify legal arguments in forensic BNs. This establishes a formal con- nection between probabilistic and argumentative reasoning. Developing such a method is ultimately aimed at supporting legal experts in their decision making process