In place of the traditional epistemological view of knowledge as justified true belief we argue that artificial intelligence and law needs an evidence-based epistemology according to which scientific knowledge is based on critical analysis of evidence using argumentation. This new epistemology of scientific evidence (ESE) models scientific knowledge as achieved through a process of marshaling evidence in a scientific inquiry that results in a convergence of scientific theories and research results. We show how a dialogue interface of argument from expert (...) opinion, along with its set of critical questions, provides the argumentation component of the ESE. It enables internal scientific knowledge to be translated over into a wider arena in which individual nonexpert citizens and groups can make use of it. The external component shows how evidence is presented and used in a legal procedural setting that includes fact-finding, weighing the credibility of expert witnesses, and critical questioning of arguments. The paper critically reviews the standards of admissibility of scientific evidence using the ESE. (shrink)

A large body of research in cognitive science differentiates human reasoning into two types: fast, intuitive, and emotional “System 1” thinking, and slower, more reflective “System 2” reasoning. According to this research, human reasoning is by default fast and intuitive, but that means that it is prone to error and biases that cloud our judgments and decision making. To improve the quality of reasoning, critical thinking education should develop strategies to slow it down and to become more reflective. The goal (...) of such education should be to enable and motivate students to identify weaknesses, gaps, biases, and limiting perspectives in their own reasoning and to correct them. This contribution discusses how this goal could be achieved with regard to reasoning that involves the construction of arguments; or more precisely: how computer-supported argument visualization tools could be designed that support reflection on the quality of arguments and their improvement. Three types of CSAV approaches are distinguished that focus on reflection and self-correcting reasoning. The first one is to trigger reflection by confronting the user with specific questions that direct attention to critical points. The second approach uses templates that, on the one hand, provide a particular structure to reason about an issue by means of arguments and, on the other, include prompts to enter specific items. And a third approach is realized in specifically designed user guidance that attempts to trigger reflection and self-correction. These types of approaches are currently realized only in very few CSAV tools. In order to inform the future development of what I call reflection tools, this article discusses the potential and limitations of these types and tools with regard to five explanations of the observation that students hardly ever engage in substantial revisions of what they wrote: a lack of strategies how to do it; cognitive overload; certain epistemic beliefs; myside bias; and over-confidence in the quality of one’s own reasoning. The question is: To what degree can each of the CSAV approaches and tools address these five potential obstacles to reflection and self-correction? (shrink)