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The opportunity prior : A simple and practical solution to the prior probability problem for legal cases

Fenton, Norman; Lagnado, David; Dahlman, Christian LU and Neil, Martin (2017) 16th International Conference on Artificial Intelligence and Law, ICAIL 2017 p.69-78
Abstract

One of the greatest impediments to the use of probabilistic reasoning in legal arguments is the difficulty in agreeing on an appropriate prior probability for the ultimate hypothesis, (in criminal cases this is normally “Defendant is guilty of the crime for which he/she is accused”). Even strong supporters of a Bayesian approach prefer to ignore priors and focus instead on considering only the likelihood ratio (LR) of the evidence. But the LR still requires the decision maker (be it a judge or juror during trial, or anybody helping to determine beforehand whether a case should proceed to trial) to consider their own prior; without it the LR has limited value. We show that, in a large class of cases, it is possible to arrive at a... (More)

One of the greatest impediments to the use of probabilistic reasoning in legal arguments is the difficulty in agreeing on an appropriate prior probability for the ultimate hypothesis, (in criminal cases this is normally “Defendant is guilty of the crime for which he/she is accused”). Even strong supporters of a Bayesian approach prefer to ignore priors and focus instead on considering only the likelihood ratio (LR) of the evidence. But the LR still requires the decision maker (be it a judge or juror during trial, or anybody helping to determine beforehand whether a case should proceed to trial) to consider their own prior; without it the LR has limited value. We show that, in a large class of cases, it is possible to arrive at a realistic prior that is also as consistent as possible with the legal notion of ‘innocent until proven guilty’. The approach can be considered as a formalisation of the ‘island problem’ whereby if it is known the crime took place on an island when n people were present, then each of the people on the island has an equal prior probability 1/n of having carried out the crime. Our prior is based on simple location and time parameters that determine both a) the crime scene/time (within which it is certain the crime took place) and b) the extended crime scene/time which is the ‘smallest’ within which it is certain the suspect was known to have been ‘closest’ in location/time to the crime scene. The method applies to cases where we assume a crime has taken place and that it was committed by one person against one other person (e.g. murder, assault, robbery). The paper considers both the practical and legal implications of the approach. We demonstrate how the opportunity prior probability is naturally incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Bayesian networks, Crime scene, Island problem, Opportunity prior probability, Prior probability, Time
host publication
Proceedings of the 16th International Conference on Artificial Intelligence and Law, ICAIL 2017
pages
10 pages
publisher
Association for Computing Machinery (ACM)
conference name
16th International Conference on Artificial Intelligence and Law, ICAIL 2017
conference location
London, United Kingdom
conference dates
2017-06-12 - 2017-06-16
external identifiers
  • scopus:85045903381
ISBN
9781450348911
DOI
10.1145/3086512.3086519
language
English
LU publication?
yes
id
e0e4bfc3-1007-4328-a180-b27863a21f03
date added to LUP
2018-05-07 12:25:57
date last changed
2019-10-15 06:37:21
@inproceedings{e0e4bfc3-1007-4328-a180-b27863a21f03,
  abstract     = {<p>One of the greatest impediments to the use of probabilistic reasoning in legal arguments is the difficulty in agreeing on an appropriate prior probability for the ultimate hypothesis, (in criminal cases this is normally “Defendant is guilty of the crime for which he/she is accused”). Even strong supporters of a Bayesian approach prefer to ignore priors and focus instead on considering only the likelihood ratio (LR) of the evidence. But the LR still requires the decision maker (be it a judge or juror during trial, or anybody helping to determine beforehand whether a case should proceed to trial) to consider their own prior; without it the LR has limited value. We show that, in a large class of cases, it is possible to arrive at a realistic prior that is also as consistent as possible with the legal notion of ‘innocent until proven guilty’. The approach can be considered as a formalisation of the ‘island problem’ whereby if it is known the crime took place on an island when n people were present, then each of the people on the island has an equal prior probability 1/n of having carried out the crime. Our prior is based on simple location and time parameters that determine both a) the crime scene/time (within which it is certain the crime took place) and b) the extended crime scene/time which is the ‘smallest’ within which it is certain the suspect was known to have been ‘closest’ in location/time to the crime scene. The method applies to cases where we assume a crime has taken place and that it was committed by one person against one other person (e.g. murder, assault, robbery). The paper considers both the practical and legal implications of the approach. We demonstrate how the opportunity prior probability is naturally incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case.</p>},
  author       = {Fenton, Norman and Lagnado, David and Dahlman, Christian and Neil, Martin},
  isbn         = {9781450348911},
  keyword      = {Bayesian networks,Crime scene,Island problem,Opportunity prior probability,Prior probability,Time},
  language     = {eng},
  location     = {London, United Kingdom},
  month        = {06},
  pages        = {69--78},
  publisher    = {Association for Computing Machinery (ACM)},
  title        = {The opportunity prior : A simple and practical solution to the prior probability problem for legal cases},
  url          = {http://dx.doi.org/10.1145/3086512.3086519},
  year         = {2017},
}