TY - GEN
T1 - Judicial Support Tool
T2 - 16th International Conference on Scalable Uncertainty Management, SUM 2024
AU - Bolonkin, Maksim
AU - Chakrabarty, Sayak
AU - Molinaro, Cristian
AU - Subrahmanian, V. S.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85210091795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210091795&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-76235-2_5
DO - 10.1007/978-3-031-76235-2_5
M3 - Conference contribution
AN - SCOPUS:85210091795
SN - 9783031762345
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 69
BT - Scalable Uncertainty Management - 16th International Conference, SUM 2024, Proceedings
A2 - Destercke, Sébastien
A2 - Martinez, Maria Vanina
A2 - Sanfilippo, Giuseppe
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 November 2024 through 29 November 2024
ER -