One of the main obstacles tri the use of Artificial Neural Network (ANN) in the legal domain comes from their inability to justify their reasoning. Justification indeed is crucial fur the judge because it assures him that the reasoning carried out by a legal machine is legally founded. We propose in this paper a method able to overcome this constraint by developing an algorithm of justification applied to connectionist prototypes (Multilayer Perceptron) implemented at the Court of Appeal of Versailles. We will first describe the algorithm. We will then discuss the two main advantages offered by the ANN with regard to rule based systems. A first advantage consists of their suitability for some types of reasoning not based on explicit rules, which are specially numerous in the discretionary field (if the judge. Another advantage can be emphasised as a result (if our experiment: these models can be used for improving the self justification process (if a decision maker (making it more precise) and even for predicting (or suggesting) new lines of reasoning based on implicit knowledge. Some examples extracted from a knowledge base on the contract of employment (clause of non-competition) will illustrate this point.
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