A Preferential DL Approach to Model the Non bis in idem Principle for the Legal Domain

Tracking #: 2447-3661

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Authors: 
Cleyton Mario de Oliveira Rodrigues
Fred Freitas
Ivan Varzinczak1
Italo Oliveira

Responsible editor: 
Guilin Qi

Submission type: 
Full Paper
Abstract: 
Description Logics (DLs) are a family of formalisms that emerged to balance the trade-off between expressiveness and decidability for classical monotonic logic. Often, the research developed under the umbrella of AI & Law has relied on full synergy with DL to support argumentation reasoning, decision systems, legal compliance checking, and axiomatization of rationales and assumptions in the legal domain. Nevertheless, in many legal scenarios, regulations are defeasible. Inferences within the legal field are not purely deductive in nature, but retractable and ampliative since generalizations mostly hold for normal or typical cases. This is absolutely true in the criminal domain, where general criminal types are usually described in the caput of the norms (e.g., a robbery), and other specific types unfold from these (e.g., robbery followed by death, which is known as “Latrocínio” in Brazilian Criminal law). Although the classical subsumption relation may seem a correct way to model the hierarchy of laws at first glance, if no contradiction arises between the more general and more specific, what it should be pointed out that the penalty for specific crimes cancel out the penalties foreseen by the more general laws. In other words, a hierarchy of norms must not rely on classical subsumption relation; instead, a non-monotonic approach suits better in this setting. Therefore, in this paper, we show that Preferential DL, a defeasible version of Description Logic, is better suited than classical DLs for a faithful representation of the content of legal regulatory knowledge; in particular, w.r.t. the representation of the principle of Double Jeopardy (a.k.a. Non/Ne bis in idem). In this paper, we make the case for the application of ontologies represented in defeasible, preferential DLs for modelling laws and penalties. Our solution focuses on overrule relations to organize a set of defeasible axioms in terms of specificity criteria.
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