Can criminal justice be predicted? Using regression analysis to predict judges’ decisions on petitions for new criminal trials

Lidén, Moa

doi:10.1016/j.scijus.2023.12.001

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…This threat may also emerge if cases with the same or similar enough material circumstances are treated differently [18], [22]- [24] and if prosecutors and judges could create new crimes in order to punish behavior they do not condone. However, as new applications of AI for use in the legal field are being developed at a steady pace [25]- [27] another type of predictability has arisen and this form is not viewed exclusively positively [28]- [34]. The crime scene has long ceased to be a combination of time, place and evidence where people were.…”

Section: Introductionmentioning

confidence: 99%

“…Apparently, the origin of the ban can be traced back partly to comparisons that used any type of machine learning (ML) in reference to behavior at individual judge level for asylum cases finding massive differences between people involved [41]. In the US as well, problems of ownership and copyright over Court decision data have led to legal wrangles [34], [42], [43]. Los Angeles, a city that is often viewed as the home of dreams full of aspirations finds itself in the tight grip of this criminal system nexus with global poverty inequality and urban development [44].…”

Section: Introductionmentioning

confidence: 99%

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Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence

Mihna,

Habeeb,

Khaleel

et al. 2024

Mesopotamian Journal of CyberSecurity

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With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizingsubtle periodic patterns and uncovering relationships among the collected datasets. Through thiscomprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations infrequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates theefficacy of the AI model in generating productive insights and providing the most accurate predictionsof future criminal trends. These predictive insights are poised to revolutionize the strategies of lawenforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethicalconsiderations, this research ensures the continued feasibility of AI use while safeguarding individuals'constitutional rights, including privacy. The anticipated outcomes of this research are anticipated tofurnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in theidentification of effective crime prevention strategies. By harnessing the potential of AI, this researchcontributes to the promotion of proactive strategies and data-driven models in crime analysis andprediction, offering a promising avenue for enhancing public security in Los Angeles and othermetropolitan areas.

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