Evidence Types, Credibility Factors, and Patterns for Weighing Conflicting Evidence

 In Argument Structures, Automating Argument Mining, Data Projects, General Methodology, Legal Reasoning, Presentations, Products, Projects, Publications, Reasoning - Causal Nexus, Reasoning - In-Service Aggravation, Reasoning - In-Service Incurrence, Reasoning - Present Disability, Semantic Data, Service Connection Rule Tree, Software Applications, VetClaim Corpus, Veterans Claims Project, Veterans Claims Reasoning

Evidence Types, Credibility Factors, and Patterns or Soft Rules for Weighing Conflicting Evidence: Argument Mining in the Context of Legal Rules Governing Evidence Assessment, Vern R. Walker, Dina Foerster, Julia Monica Ponce, and Matthew Rosen, in the Proceedings of the 5th Workshop on Argument Mining (ArgMining 2018), pp. 68 – 78, held at Brussels, Belgium, on November 1, 2018 (ACL 2018).

The presentation poster is also available for download.

Abstract

This paper reports on the results of an empirical study of adjudicatory decisions about veterans’ claims for disability benefits in the United States. It develops a typology of kinds of relevant evidence (argument premises) employed in cases, and it identifies factors that the tribunal considers when assessing the credibility or trustworthiness of individual items of evidence. It also reports on patterns or “soft rules” that the tribunal uses to comparatively weigh the probative value of conflicting evidence. These evidence types, credibility factors, and comparison patterns are developed to be inter-operable with legal rules governing the evidence assessment process in the U.S. This approach should be transferable to other legal and non-legal domains.

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