De-biasing role induced bias using Bayesian networks. (22nd October 2019)

• Record Type: Journal Article
• Authors: Schweizer, Mark
• Is Part Of: Law, probability & risk. Volume 18:Number 4(2019)
• Page Start: 255
• Page End: 273
• Publication Date: 2019-10-22
• Subjects: Bayes' nets -- cognitive bias -- role induced bias -- decision aid -- subjective probability theory -- experimental study -- de-biasing; Proximate cause (Law) -- Periodicals; Risk -- Periodicals; Law -- Mathematical models -- Periodicals; Law -- Methodology -- Periodicals; Probabilities -- Periodicals; Risk assessment -- Periodicals; Law -- Mathematical models; Law -- Methodology; Probabilities; Proximate cause (Law); Risk; Risk assessment; Periodicals; 340.1
• DOI: 10.1093/lpr/mgz015
• Languages: English
• ISSNs: 1470-8396

Abstract

Abstract: The merits of using subjective probability theory as a normative standard for evidence evaluation by legal fact-finders have been hotly debated for decades. Critics argue that formal mathematical models only lead to an apparent precision that obfuscates the ad-hoc nature of the many assumptions that underlie the model. Proponents of using subjective probability theory as normative standard for legal decision makers, specifically proponents of using Bayesian networks as decision aids in complex evaluations of evidence, must show that formal models have tangible benefits over the more natural, holistic assessment of evidence by explanatory coherence. This article demonstrates that the assessment of evidence using a Bayesian network parametrized with values obtained from the decision makers reduces role-induced bias, a bias that has been largely resistant to de-biasing attempts so far.