4.
Steging C; van Leeuwen L
A hybrid approach to legal textual entailment Proceedings Article
In: Eighteenth International Workshop on Juris-Informatics (JURISIN 2024), Hamamatsu, Japan, 2024.
@inproceedings{StegingJURISIN2024,
title = {A hybrid approach to legal textual entailment},
author = {Steging, C. and
{van Leeuwen}, L.},
url = {https://jurisinformaticscenter.github.io/jurisin2024/jurisin2024local_proceedings.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Eighteenth International Workshop on Juris-Informatics (JURISIN 2024)},
address = {Hamamatsu, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
3.
Van Leeuwen L; Verbrugge R; Verheij B; Renooij S
Building a Stronger Case: Combining Evidence and Law in Scenario-Based Bayesian Networks Proceedings Article
In: 3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024, pp. 291–299, IOS Press 2024.
@inproceedings{van2024building,
title = {Building a Stronger Case: Combining Evidence and Law in Scenario-Based Bayesian
Networks},
author = {Van Leeuwen, Ludi and Verbrugge, Rineke and Verheij, Bart and Renooij, Silja},
year = {2024},
date = {2024-01-01},
booktitle = {3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI
2024},
pages = {291–299},
organization = {IOS Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
van Leeuwen L; Verheij B; Verbrugge R; Renooij S
Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios. Proceedings Article
In: The Nineteenth International Conference on Artificial Intelligence and Law (ICAIL 2023). Proceedings of the Conference, ACM, New York, NY, USA, Braga, Portugal, 2023.
@inproceedings{vanLeeuwen2023,
title = {Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios. },
author = {Ludi van Leeuwen and Bart Verheij and Rineke Verbrugge and Silja Renooij},
doi = {https://doi.org/10.1145/3594536.3595125},
year = {2023},
date = {2023-06-01},
booktitle = {The Nineteenth International Conference on Artificial Intelligence and Law (ICAIL 2023). Proceedings of the Conference},
publisher = {ACM, New York, NY, USA},
address = {Braga, Portugal},
abstract = {Scenario-based Bayesian networks (BNs) have been proposed as a tool for the rational handling of evidence. The proper evaluation of existing methods requires access to a ground truth that can be used to test the quality and usefulness of a BN model of a crime. However, that would require a full probability distribution over all relevant variables used in the model, which is in practice not available. In this paper, we use an agent-based simulation as a proxy for the ground truth for the evaluation of BN models as tools for the rational handling of evidence. We use fictional crime scenarios as a background. First, we design manually constructed BNs using existing design methods in order to model example crime scenarios. Second, we build an agent-based simulation covering the scenarios of criminal and non-criminal behavior. Third, we algorithmically determine BNs using statistics collected experimentally from the agent-based simulation that represents the ground truth. Finally, we compare the manual, scenario-based BNs to the algorithmic BNs by comparing the posterior probability distribution over outcomes of the network to the ground-truth frequency distribution over those outcomes in the simulation, across all evidence valuations. We find that both manual BNs and algorithmic BNs perform similarly well: they are good reflections of the ground truth in most of the evidence valuations. Using ABMs as a ground truth can be a tool to investigate Bayesian Networks and their design methods, especially under circumstances that are implausible in real-life criminal cases, such as full probabilistic information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Scenario-based Bayesian networks (BNs) have been proposed as a tool for the rational handling of evidence. The proper evaluation of existing methods requires access to a ground truth that can be used to test the quality and usefulness of a BN model of a crime. However, that would require a full probability distribution over all relevant variables used in the model, which is in practice not available. In this paper, we use an agent-based simulation as a proxy for the ground truth for the evaluation of BN models as tools for the rational handling of evidence. We use fictional crime scenarios as a background. First, we design manually constructed BNs using existing design methods in order to model example crime scenarios. Second, we build an agent-based simulation covering the scenarios of criminal and non-criminal behavior. Third, we algorithmically determine BNs using statistics collected experimentally from the agent-based simulation that represents the ground truth. Finally, we compare the manual, scenario-based BNs to the algorithmic BNs by comparing the posterior probability distribution over outcomes of the network to the ground-truth frequency distribution over those outcomes in the simulation, across all evidence valuations. We find that both manual BNs and algorithmic BNs perform similarly well: they are good reflections of the ground truth in most of the evidence valuations. Using ABMs as a ground truth can be a tool to investigate Bayesian Networks and their design methods, especially under circumstances that are implausible in real-life criminal cases, such as full probabilistic information.
1.
van Leeuwen L; Verheij B; Verbrugge R; Renooij S
Evaluating Methods for Setting a Prior Probability of Guilt Proceedings Article
In: Legal Knowledge and Information Systems, pp. 63–72, IOS Press, 2023.
@inproceedings{van2023evaluating,
title = {Evaluating Methods for Setting a Prior Probability of Guilt},
author = {van Leeuwen, Ludi and Verheij, Bart and Verbrugge, Rineke and Renooij, Silja},
year = {2023},
date = {2023-01-01},
booktitle = {Legal Knowledge and Information Systems},
pages = {63–72},
publisher = {IOS Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}