The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA.The related applications fields, challenges, and most importantly the openings for future research, are detailed.
Several topics, problems, and established legal principles are already being challenged using artificial intelligence (AI) in numerous applications. The powers of AI have been snowballing to the point where it is evident that AI applications in law and various economic sectors aid in promoting a good society. However, questions such as who the prolific authors, papers, and institutions are, as well as what the specific and thematic areas of application are, remain unanswered. In the current study, 177 papers on artificial intelligence applications in law published between 1960 and April 29, 2022, were pulled from Scopus using keywords and analysed scientometrically. We identified the strongest citation bursts, the most prolific authors, countries/regions, and primary research interests, as well as their evolution trends and collaborative relationships over the past 62 years. The analysis also identified co-authorship networks, collaboration networks of countries/regions, co-occurrence networks of keywords, and timeline visualization of keywords. This study concludes that systematic study and enough attention are still lacking in artificial intelligence application in law (AIL). The methodical design of the required platforms, as well as the collecting, cleansing, and storage of data; the collaboration of many stakeholders, researchers, and nations/regions; are all problems that AIL must still overcome. Both researchers and industry professionals who are devoted to AIL will find value in the findings.
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