
The starting point of this article is the claim that logics for defeasible argumentation provide the means to logically characterise the difference between several kinds of proof burdens, but only if they are embedded in a dynamic setting that captures the various stages of a legal proceeding. It is also argued that ëstandardí argumentation logics for AI must be adapted in order to model shifts in burdens of proof. Thus this analysis illustrates in two ways that logics cannot be simply imposed on the law but that features of legal systems must be taken into account. First there is the claim that the burden of persuasion, which legally is the burden to prove a statement to a specified degree (the standard of proof) on the penalty of losing on the issue, can be verified by applying an argumentation logic to the evidence available at the final stage of a proceeding. Then a precise distinction is made between two burdens that are sometimes confused, namely the burden of production and the tactical burden. In this analysis, the tactical burden of proof is automatically induced by the defeasible nature of the reasoning. The burden of production, by contrast, concerns the legal question whether an issue can be submitted to trial or must be decided as a matter of law against the one who fails to produce any evidence. Finally the issue is raised to what extent this account can be generalised to statistical and story-based approaches.
DEFEASIBLE ARGUMENTATION; BURDEN OF PROOF; ARTIFICIAL INTELLIGENCE; ARGUMENTATION LOGIC
DEFEASIBLE ARGUMENTATION; BURDEN OF PROOF; ARTIFICIAL INTELLIGENCE; ARGUMENTATION LOGIC
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
