## Abstract

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.

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.

Original language | English |
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Pages (from-to) | 475-494 |

Journal | International Journal of Approximate Reasoning |

Volume | 80 |

DOIs | |

Publication status | Published - Jan 2017 |

## Keywords

- Bayesian networks
- argumentation
- probabilistic reasoning
- explanation
- inference
- uncertainty