One-Shot AI Machine Learning Needed When Gleaning Landmark Legal Cases

2022 ◽  
Author(s):  
Lance Eliot
Keyword(s):  
Machine Learning ◽  
Legal Cases

An intelligent approach for automated argument based legal text recognition and summarization using machine learning

2021 ◽  
pp. 1-10
Author(s):  
Riya Sil ◽  
Alpana ◽  
Abhishek Roy ◽  
Mili Dasmahapatra ◽  
Debojit Dhali
Keyword(s):  
Machine Learning ◽  
Time Frame ◽  
Structured Data ◽  
Computational Time ◽  
Legal Cases ◽  
Legal Professionals ◽  
Large Numbers ◽  
Successful Execution ◽  
Legal Model ◽  
Intelligent Approach

It is essential to provide a structured data feed to the computer to accomplish any task so that it can process flawlessly to generate the desired output within minimal computational time. Generally, computer programmers should provide a structured data feed to the computer program for its successful execution. The hardcopy document should be scanned to generate its corresponding computer-readable softcopy version of the file. This process also proves to be a budget-friendly approach to disengage human resources from the entire process of record maintenance. Due to this automation, the workload of existing manpower is reduced to a significant level. This concept may prove beneficial for the delivery of any type of services to the ultimate beneficiary (i.e., citizen) in a minimal time frame. The administration has to deal with various issues of citizens due to the pressure of a huge population who seek legal help to resolve their issues, thereby leading to the filing of large numbers of pending legal cases at several courts of the country. To assist the victims with prompt delivery of justice and legal professionals in reducing their workload, this paper proposed a machine learning based automated legal model to enhance the efficiency of the legal support system with an accuracy of 94%.

scholarly journals Integrating Domain Knowledge in AI-Assisted Criminal Sentencing of Drug Trafficking Cases

2020 ◽  
Author(s):  
Tien-Hsuan Wu ◽  
Ben Kao ◽  
Anne S.Y. Cheung ◽  
Michael M.K. Cheung ◽  
Chen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knowledge Management ◽  
Drug Trafficking ◽  
Domain Knowledge ◽  
Legal Knowledge ◽  
Criminal Sentencing ◽  
Legal Cases ◽  
Legal Rules ◽  
Legal Domain ◽  
Legal Judgments

Judgment prediction is the task of predicting various outcomes of legal cases of which sentencing prediction is one of the most important yet difficult challenges. We study the applicability of machine learning (ML) techniques in predicting prison terms of drug trafficking cases. In particular, we study how legal domain knowledge can be integrated with ML models to construct highly accurate predictors. We illustrate how our criminal sentence predictors can be applied to address four important issues in legal knowledge management, which include (1) discovery of model drifts in legal rules, (2) identification of critical features in legal judgments, (3) fairness in machine predictions, and (4) explainability of machine predictions.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Machine learning

AccessScience ◽  
2018 ◽  
Keyword(s):  
Machine Learning

Experts and High Profile Infant Death Legal Cases

2008 ◽  
Author(s):  
David Spicer
Keyword(s):  
Infant Death ◽  
Legal Cases ◽  
High Profile
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