Legal idioms: a framework for evidential reasoning

Abstract

How do people make legal judgments based on complex bodies of interrelated evidence? This paper outlines a novel framework for evidential reasoning using causal idioms. These idioms are based on the qualitative graphical component of Bayesian networks, and are tailored to the legal context. They can be combined and reused to model complex bodies of legal evidence. This approach is applied to witness and alibi testimony, and is illustrated with a real legal case. We show how the framework captures critical aspects of witness reliability, and the potential interrelations between witness reliabilities and other hypotheses and evidence. We report a brief empirical study on the interpretation of alibi evidence, and show that people's intuitive inferences fit well with the qualitative aspects of the idiom-based framework.

The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach

“…In addition to developmental psychology, other research areas that have been interested in these concepts include the following: judgment and decision making (e.g., Birnbaum & Mellers, ; Birnbaum & Stegner, ), reasoning research (Stevenson & Over, ; Wolf, Rieger, & Knauff, ), and social psychological research into persuasion and attitude change (e.g., Brinol & Petty, ; Chaiken, ; Hovland, Janis, & Kelley, ; McGuire, ; O'Hara, Netemeyer, & Burton, ; Petty & Cacioppo, ; Pornpitakpan, —also incorporating direct applied research in advertising, for example, Braunsberger & Munch, ; Ohanian, ; Wiener & Mowen, ). Moreover, the concepts of trust and expertise are also of vital importance in the evaluation of legal testimony, and research has concerned both formalizations of how testimony should be viewed (e.g., Friedman, ; Hahn, Oaksford, & Harris, ; Lagnado, Fenton, & Neil, ; Schum, , ; Walton, ), and descriptive studies investigating the degree to which people are sensitive to different relevant aspects of a witness's testimony (e.g., Eaton & O'Callaghan, ; ForsterLee, Horowitz, Athaide‐Victor, & Brown, ; Harris & Hahn, ; Krauss & Sales, ; see Wells & Olson, , for a review). The importance of trust and expertise for humans, and hence its interest for researchers in psychology, predicts its importance for artificial intelligence systems, and hence its interest for computer scientists.…”

An integrated theory of causal stories and evidential arguments

“…Interestingly, many of the tentative proposed solutions to these questions originate from qualitative, (semi-)formal logical approaches. For example, the structures for Bayesian arguments proposed by [9] draw from work in argumentation theory [24], and the 'scenario schemes' for Bayesian networks in [22] were directly preceded by logical approaches to story schemes based on early qualitative work in AI [18]. Hence, it seems that a formal, logical theory is a good intermediary between on the on hand the informal, more natural conceptions of stories and arguments and on the other hand the quantitative mathematical models.…”

Causal analysis for attributing responsibility in legal cases

“…The framework complements other recent work in legal reasoning that deals, for example, with causal explanatory Bayesian networks for legal reasoning and probabilistic evaluation of evidence [12,29,34], and more generic models of legal argumentation [7]. That work can be considered most relevant at (i) the evidence gathering stage [11,34] (for example, to help the Crown Prosecution Service determine whether there is sufficient evidence for a likely conviction) and (ii) in helping lawyers understand and present evidence.…”