The opportunity prior : A simple and practical solution to the prior probability problem for legal cases
(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.
(Less)
- author
- Fenton, Norman ; Lagnado, David ; Dahlman, Christian LU and Neil, Martin
- organization
- publishing date
- 2017-06-12
- 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
- 2022-04-25 07:14:40
@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}},
booktitle = {{Proceedings of the 16th International Conference on Artificial Intelligence and Law, ICAIL 2017}},
isbn = {{9781450348911}},
keywords = {{Bayesian networks; Crime scene; Island problem; Opportunity prior probability; Prior probability; Time}},
language = {{eng}},
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}},
doi = {{10.1145/3086512.3086519}},
year = {{2017}},
}