Opening the Black Box: How Boolean AI can Support Legal Analysis

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Fältnamn	Indikatorer	Metadata
000		03191naa a2200349 4500
001		oai:research.chalmers.se:9fcf01d1-612b-4f66-a4d0-66baeca3751a
003		SwePub
008		240819s2024 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://research.chalmers.se/publication/5424372 URI
024	7	a https://doi.org/10.1109/CCAI61966.2024.106030172 DOI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a kon2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Garzo, Graziau Università degli Studi di Siena,University of Siena4 aut
245	1 0	a Opening the Black Box: How Boolean AI can Support Legal Analysis
264	1	c 2024
520		a In crime scene scenarios, there are various factors to consider when determining a suspect's guilt. However, the process of extracting and assessing these factors can be time-consuming, often taking years and incurring significant legal expenses. Judges are now exploring the potential of artificial intelligence techniques and machine learning computations within the justice system. Specifically, in the realm of criminal justice, these methodologies have the potential to aid in investigations and decision-making processes. Utilizing machine learning approaches can thus expedite the bureaucratic process, potentially making it more efficient. We introduce an idea of an approach that could provide fast and explainable support in the evaluation of guilt. Our approach relies on computations based on the presence or absence of 44 features describing the crime scene. Then, by a boolean function, we determined the final verdict of the legal case (only a subset of the extracted features are relevant to evaluate the guilt prediction). To demonstrate the practicality of our proposal, we conducted experiments based on 79 road homicide cases in Italy. As a consequence, the boolean evaluation was done according to Italian law principles. With our system, we reached a 83.2 % accuracy rate in extracting features from the legal ruling texts and a 69.6% accuracy in guilt prediction.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Systemvetenskap, informationssystem och informatik0 (SwePub)102022 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Information Systems0 (SwePub)102022 hsv//eng
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
653		a Boolean AI
653		a Machine Learning
653		a Decision Making
700	1	a Ribes, Stefano,d 1992u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)ribes
700	1	a Palumbo, Alessandrou Université de Rennes 1,University of Rennes 14 aut
710	2	a Università degli Studi di Sienab Chalmers tekniska högskola4 org
773	0	t 2024 4th International Conference on Computer Communication and Artificial Intelligence, CCAI 2024g , s. 269-272q <269-272
856	4 8	u https://research.chalmers.se/publication/542437
856	4 8	u https://doi.org/10.1109/CCAI61966.2024.10603017

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