Abstract
Judges sometimes make mistakes. We propose JUST, a logical framework within which judges can record propositions about a case and witness statements where a witness says that certain propositions are true or false. JUST allows the judge (or a jury) to assign a rating of credibility to witness statements. A world is an assignment of true/false to each proposition, which is required to satisfy case-specific integrity constraints. We first develop JUST ’s explicit algorithm, which calculates the k most likely worlds without using independence assumptions between propositions. The judge may use these calculated top-k most likely worlds to make her final decision. For this computation, JUST uses a suite of “combination” functions. We also develop JUST ’s implicit algorithm, which is far more efficient. We test JUST on 5 real-world court cases and 19 TV court cases, showing that JUST works well in practice.
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A probability distribution over \({\textsf{PW}}\) is a function \({\rho }_{\textsf{IC}}:{\textsf{PW}}\rightarrow [0,1]\) s.t. \(\Sigma _{{\gamma }\in {\textsf{PW}}}\ {\rho }_{\textsf{IC}}({\gamma })=1\).
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Acknowledgement
Cristian Molinaro acknowledges the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU.
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Bolonkin, M., Chakrabarty, S., Molinaro, C., Subrahmanian, V.S. (2025). Judicial Support Tool: Finding the k Most Likely Judicial Worlds. In: Destercke, S., Martinez, M.V., Sanfilippo, G. (eds) Scalable Uncertainty Management. SUM 2024. Lecture Notes in Computer Science(), vol 15350. Springer, Cham. https://doi.org/10.1007/978-3-031-76235-2_5
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