Inside the Juror (Hastie 1994) was, in a sense, a point of arrival for research developing formalisms that describe judicial decision making. Meter-based models of various kinds were mature, and even ready for giving way to such models that would concern themselves with the narrative content of the cases at hand, that a court is called to decide upon. Moreover, excessive emphasis was placed on lay factfinders, i.e. on jurors. It is noticeable that as “AI & Law” has become increasingly concerned with evidence in recent years - with efforts coordinated by Nissan & Martino, Zeleznikow, and others-the baggage of the meter-based models from jury research does not appear to be exploited. In this article, we try to combine their tradition with a technique of belief revision from artificial intelligence, in an attempt to provide an architectural component that would be complementary to models that apply representations or reasoning to legal narrative content.
Savaging the Spirit of the Meter-Model Tradition: A Model of Belief Revision by way of an Abstract Idealization of Responce to an Incoming Evidence Delivery During the Construction of Proof in Court / Dragoni, Aldo Franco; Nissan, E.. - In: APPLIED ARTIFICIAL INTELLIGENCE. - ISSN 0883-9514. - STAMPA. - 18:3-4(2004), pp. 277-303. [10.1080/08839510490279889]
Savaging the Spirit of the Meter-Model Tradition: A Model of Belief Revision by way of an Abstract Idealization of Responce to an Incoming Evidence Delivery During the Construction of Proof in Court
DRAGONI, Aldo Franco;
2004-01-01
Abstract
Inside the Juror (Hastie 1994) was, in a sense, a point of arrival for research developing formalisms that describe judicial decision making. Meter-based models of various kinds were mature, and even ready for giving way to such models that would concern themselves with the narrative content of the cases at hand, that a court is called to decide upon. Moreover, excessive emphasis was placed on lay factfinders, i.e. on jurors. It is noticeable that as “AI & Law” has become increasingly concerned with evidence in recent years - with efforts coordinated by Nissan & Martino, Zeleznikow, and others-the baggage of the meter-based models from jury research does not appear to be exploited. In this article, we try to combine their tradition with a technique of belief revision from artificial intelligence, in an attempt to provide an architectural component that would be complementary to models that apply representations or reasoning to legal narrative content.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.