Human-annotated rationales and explainable text classification: a survey.

Asking annotators to explain "why" they labeled an instance yields annotator rationales: natural language explanations that provide reasons for classifications. In this work, we survey the collection and use of annotator rationales. Human-annotated rationales can improve data quality and form a valuable resource for improving machine learning models.

Editors' Review and Introduction: Models of Rational Proof in Criminal Law.

Decisions concerning proof of facts in criminal law must be rational because of what is at stake, but the decision-making process must also be cognitively feasible because of cognitive limitations, and it must obey the relevant legal-procedural constraints. In this topic three approaches to rational reasoning about evidence in criminal law are compared in light of these demands: arguments, probabilities, and scenarios. This is done in six case studies in which different authors analyze a manslaughter case from different theoretical perspectives, plus four commentaries on these case studies.

The Hybrid Theory of Stories and Arguments Applied to the Simonshaven Case.

This paper presents the hybrid theory of stories and arguments for reasoning with evidence in legal cases and applies this theory to the Simonshaven case. In the hybrid theory, alternative hypothetical stories about "what happened" in a case are constructed and discussed in a dialectical process of argument and counterargument. After informally explaining stories, arguments, and the ways in which they interact, this paper gives a method for rational proof based on critical questions and shows how this method can be used in the Simonshaven case.