De-Biasing Role Induced Bias Using Bayesian Networks
Mark Schweizer, De-Biasing Role Induced Bias Using Bayesian Networks, Law, Probability and Risk, Volume 18, Issue 4, December 2019, Pages 255–273, Doi.org/10.1093/lpr/mgz015 (revised version)
22 Pages Posted: 27 Aug 2018 Last revised: 3 Feb 2020
Date Written: April 26, 2018
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 paper demonstrates that the assessment of evidence using a Bayesian network parametrized with values obtained from the decision makers greatly reduces role-induced bias, a bias that has been largely resistant to de-biasing attempts so far. This shows that using Bayesian networks as decision aids can benefit legal decision making.
Keywords: evidence law, bayesian network, role-induced bias
JEL Classification: K41
Suggested Citation: Suggested Citation