On Modelling Non-Probabilistic Uncertainty In The Likelihood Ratio Approach To Evidential Reasoning
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 88970645
- Type
- D - Journal article
- DOI
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10.1007/s10506-014-9157-3
- Title of journal
- Artificial Intelligence and Law
- Article number
- -
- First page
- 239
- Volume
- 22
- Issue
- 3
- ISSN
- 0924-8463
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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0
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents the first method to build conditional probability distributions for a Bayesian network from computational models of argumentation, enabling such probability distributions to be validated. Such justification of probability distribution is a critical barrier to their more widespread adoption as a decision support tool in areas such as law. The approach is grounded in an extensive review of existing Bayesian network models tackling real-world legal and forensic evidential reasoning problems. Researchers at Utrecht University have employed this approach with additional modelling schemes based on their experience as an expert witness in criminal court (https://doi.org/10.1007/s10506-018-9235-z).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -