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Artificial Intelligence and Law

, Volume 25, Issue 1, pp 5–27 | Cite as

Data-centric and logic-based models for automated legal problem solving

  • L. Karl BrantingEmail author
Article

Abstract

Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges of formalizing legal rules and matching legal predicates with the language of ordinary parlance by exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each.

Keywords

Logical representation Empirical methods Prediction Hybrid reasoning 

Notes

Acknowledgements

The MITRE Corporation is a not-for-profit Federally Funded Research and Development Center chartered in the public interest. This document is approved for Public Release, Distribution Unlimited, Case Number 16-4565 ©2016 The MITRE Corporation. All rights reserved.

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© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.The MITRE CorporationMcLeanUSA

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