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Evaluating Human versus Machine Learning Performance in a LegalTech Problem

MONTANA Knowledge Management Ltd., H-1097 Budapest, Hungary
Doctoral School of Law, Eötvös Loránd University Egyetem Square 1-3., H-1053 Budapest, Hungary
Doctoral School in Linguistics, University of Szeged, Egyetem Street 2., H-6722 Szeged, Hungary
Institute of the Information Society, National University of Public Service, H-1083 Budapest, Hungary
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;
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 legal tech; data analytics; artificial intelligence; Industry 4.0
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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.

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.

Chicago/Turabian Style

Orosz, 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.

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