LEGAL AI: A Tool to help Lawyers Optimize their Performance

The modern legal environment where quick changes are happening requires integrating advanced technologies to make legal professionals more efficient and effective. The purpose of this project is to propose "LegalAI," which could solve some of the critical challenges legal software and hardware lawyers currently face, such as document analysis, language translation, and privacy. Legal AI’s primary concern is the difficulty and time- intensive nature of reviewing extensive legal documents. By synthesizing and recomposing key points, Legal AI aids the lawyer in obtaining the most important content from lengthier texts as soon as possible. This increases experts’ time and productivity. Moreover, the capabilities of legal AI include translation of the language to ensure free interaction among the lawyers from states. It will contribute to greater cohesion and collaboration within the legal cluster. There are several aspects related to the privacy of the client’s sensitive information such as redaction. All of the abovementioned aspects contribute to the considerable opportunities of legal AI to improve the performance of the professionals. In general, legal Ai involves the integration of automated document summary, language translation, and privacy protection mechanisms for better performance of the lawyers.

Authors

Institutions

Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/iceect61758.2024.10739264
Citations
1
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
2.23
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LEGAL AI: A Tool to help Lawyers Optimize their Performance

Sanchi Shah, Neha Agarwal, A.G. de Almeida, Neer Bhavesh Khandor
1 citations
Artificial Intelligence in Law
2.23
article

LEGAL AI: A Tool to help Lawyers Optimize their Performance

Sanchi Shah, Neha Agarwal, A.G. de Almeida, Neer Bhavesh Khandor
article en
1 citations

Abstract

The modern legal environment where quick changes are happening requires integrating advanced technologies to make legal professionals more efficient and effective. The purpose of this project is to propose "LegalAI," which could solve some of the critical challenges legal software and hardware lawyers currently face, such as document analysis, language translation, and privacy. Legal AI’s primary concern is the difficulty and time- intensive nature of reviewing extensive legal documents. By synthesizing and recomposing key points, Legal AI aids the lawyer in obtaining the most important content from lengthier texts as soon as possible. This increases experts’ time and productivity. Moreover, the capabilities of legal AI include translation of the language to ensure free interaction among the lawyers from states. It will contribute to greater cohesion and collaboration within the legal cluster. There are several aspects related to the privacy of the client’s sensitive information such as redaction. All of the abovementioned aspects contribute to the considerable opportunities of legal AI to improve the performance of the professionals. In general, legal Ai involves the integration of automated document summary, language translation, and privacy protection mechanisms for better performance of the lawyers.

Dwarkadas J. Sanghvi College of Engineering (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 10%
Artificial Intelligence in Law
2.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

LEGAL AI: A Tool to help Lawyers Optimize their Performance — Sanchi Shah, Neha Agarwal, et al. · (2024) | TGRS Research Map | TGRS