Evaluating Human versus Machine Learning Performance in a LegalTech Problem
1
MONTANA Knowledge Management Ltd., H-1097 Budapest, Hungary
2
Doctoral School of Law, Eötvös Loránd University Egyetem Square 1-3., H-1053 Budapest, Hungary
3
Doctoral School in Linguistics, University of Szeged, Egyetem Street 2., H-6722 Szeged, Hungary
4
Institute of the Information Society, National University of Public Service, H-1083 Budapest, Hungary
5
Wolters Kluwer Hungary Ltd., Budafoki Way 187-189, H-1117 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Academic Editor: Juan Pavón
Appl. Sci. 2022, 12(1), 297; https://doi.org/10.3390/app12010297
Received: 13 December 2021
/
Revised: 21 December 2021
/
Accepted: 22 December 2021
/
Published: 29 December 2021
(This article belongs to the Special Issue Development and Applications of AI on Legal Tech)
Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process.
View Full-Text
Keywords:
legal tech; data analytics; artificial intelligence; Industry 4.0
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Orosz, T.; Vági, R.; Csányi, G.M.; Nagy, D.; Üveges, I.; Vadász, J.P.; Megyeri, A. Evaluating Human versus Machine Learning Performance in a LegalTech Problem. Appl. Sci. 2022, 12, 297. https://doi.org/10.3390/app12010297
AMA Style
Orosz T, Vági R, Csányi GM, Nagy D, Üveges I, Vadász JP, Megyeri A. Evaluating Human versus Machine Learning Performance in a LegalTech Problem. Applied Sciences. 2022; 12(1):297. https://doi.org/10.3390/app12010297
Chicago/Turabian StyleOrosz, Tamás, Renátó Vági, Gergely M. Csányi, Dániel Nagy, István Üveges, János P. Vadász, and Andrea Megyeri. 2022. "Evaluating Human versus Machine Learning Performance in a LegalTech Problem" Applied Sciences 12, no. 1: 297. https://doi.org/10.3390/app12010297
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
