Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios

van Leeuwen, Ludi; Verheij, Bart; Verbrugge, Rineke; Renooij, Silja

doi:10.1145/3594536.3595125

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…We assume variables are Boolean with possible values {T, F}. BNs, or sub-structures of BNs called idioms, have been proposed for representing reasoning in court cases [6,7,8,9,10,11,12]. Fenton et al proposed a BN idiom for reasoning about the opportunity prior [3], of which a simplified version is used in this paper (Figure 2, with the nodes AtCrimeScene and Guilty).…”

Section: Bayesian Networkmentioning

confidence: 99%

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Evaluating Methods for Setting a Prior Probability of Guilt

van Leeuwen,

Verheij,

Verbrugge

et al. 2023

Frontiers in Artificial Intelligence and Applications

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One way of reasoning with uncertainties in the context of law is to use probabilities. However, methods for reasoning about the probability of guilt in a court case requires us to specify a prior probability of guilt, which is the probability of guilt before any evidence is known. There is no accepted approach for specifying the prior probability of guilt but multiple solutions have been proposed. In this paper, we consider three approaches: a prior that is based on the population, a prior based on the number of agents that have similar opportunity as the suspect and a prior that represents a legal norm. For comparing and evaluating the approaches, we use an agent-based model as a ground truth in which all probabilities are known. With the data generated in the ground truth model, we investigate how the choice of prior influences the posterior probability of guilt for both guilty and innocent agents. Using a decision threshold, we can determine the effect of the three approaches on the rates of correct and incorrect convictions and acquittals. We find that the opportunity prior results in higher rates of both correct convictions and false convictions and requires more assumptions and access to data and knowledge than the legal prior and population prior.

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Section: Bayesian Networkmentioning

confidence: 99%

“…Agent-Based Models (ABMs) have been used to provide a ground truth of data for testing methods in the legal domain [8]. They provide a controlled environment in which human-like behaviour can arise due to agent-agent and agent-world interactions.…”

Section: Agent-based Modelsmentioning

confidence: 99%

Evaluating Methods for Setting a Prior Probability of Guilt

van Leeuwen,

Verheij,

Verbrugge

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

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AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law

Bex

2024

Artif Intell Law

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We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field.

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Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios

Cited by 2 publications

References 21 publications

Evaluating Methods for Setting a Prior Probability of Guilt

Evaluating Methods for Setting a Prior Probability of Guilt

AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law

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