The Application of Artificial Intelligence in Law : A Bibliometric Analysis
DOI:
https://doi.org/10.17010/pijom/2025/v18i7/174565Keywords:
bibliometric analysis, artificial intelligence, law, review.JEL Classification Codes: K2, M0, M1
Publication Chronology: Paper Submission Date : March 5, 2025 ; Paper sent back for Revision : April 5, 2025 ; Paper Acceptance Date : May 25, 2025 ; Paper Published Online : July 15, 2025
Abstract
Purpose : This research presented an extensive review of applications of artificial intelligence (AI) in law, identified existing trends, and revealed emerging themes and areas of focus in these disciplines.
Methodology : This study performed a bibliometric analysis on 1,114 articles obtained from Scopus. It employed thematic analysis, co-occurrence analysis, and citation analysis to assess the contributions of different research components.
Findings : This research illustrated the close and interdependent relationship between law and AI. Two of the emerging prominent themes were neural networks and adaptation control. Additionally, a niche theme showed how decision support systems, data mining, legal reasoning, argumentation, and optimization are vital. Moreover, AI and business law were connected with ethics, humanitarian law, case-based reasoning, decision support systems, machine learning, and big data. Among the leading publishers in this category are China, the USA, the UK, and Italy, all of whom have published a growing number of titles. The most popular keywords used were “laws and legislation,” “human,” and “machine learning.”
Practical Implications : The research contributed to the AI and business law literature. Managers and policymakers can prioritize areas based on the thematic issues that emerged from the literature. It also highlighted the areas of difficulty for organizations, such as those involving human rights and data privacy. The results are intended to guide business law regulations for AI development.
Originality : The study is unique, as it is one of the early studies that recognized the relationship between business law and AI, and recognized emerging themes and modern challenges.
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1) Al-Raggad, A. K., & Al-Raggad, M. (2024). Analyzing trends: A bibliometric study of administrative law and forensic accounting in the digital age. Heliyon, 10(18), Article ID e37462. https://doi.org/10.1016/j.heliyon.2024.e37462
2) Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
3) Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
4) Arora, P., Singh, A. B., Ahuja, V., & Kumar, R. (2025). Mapping trends and future directions in consumer engagement and loyalty: A comprehensive bibliometric and thematic analysis. Indian Journal of Marketing, 55(3), 8–33. https://doi.org/10.17010/ijom/2025/v55/i3/174831
5) Barfield, W., & Pagallo, U. (2018). Research handbook on the law of artificial intelligence. Edward Elgar Publishing.
6) Bhattacharya, S., & Mishra, B. B. (2016). Cyber atmospherics and its impact on e-retailing buyer behavior: A factor analysis. Prabandhan: Indian Journal of Management, 9(4), 30–51. https://doi.org/10.17010/pijom/2016/v9i4/90770
7) Biresaw, S. M., & Saste, A. U. (2022). The impacts of artificial intelligence on research in the legal profession. International Journal of Law and Society, 5(1), 53–65. https://doi.org/10.11648/j.ijls.20220501.17
8) Buiten, M. C. (2019). Towards intelligent regulation of artificial intelligence. European Journal of Risk Regulation, 10(1), 41–59. https://doi.org/10.1017/err.2019.8
9) Calo, R. (2017). Artificial intelligence policy: A primer and roadmap. SSRN. https://doi.org/10.2139/ssrn.3015350
10) Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & Society, 36, 1105–1116. https://doi.org/10.1007/s00146-021-01162-8
11) Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593
12) Dixit, A., Jha, R., Baber, R., & Baber, P. (2024). The impact of artificial intelligence on digital employee engagement. Prabandhan: Indian Journal of Management, 17(9), 24–43. https://doi.org/10.17010/pijom/2024/v17i9/173940
13) Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
14) Ejjami, R. (2024). AI-driven justice: Evaluating the impact of artificial intelligence on legal systems. International Journal for Multidisciplinary Research, 6(3), 1–29. https://doi.org/10.36948/ijfmr.2024.v06i03.23969
15) Kim, B., & Doshi-Velez, F. (2021). Machine learning techniques for accountability. AI Magazine, 42(1), 47–52. https://doi.org/10.1002/j.2371-9621.2021.tb00010.x
16) Kulshrestha, V., & Jain, K. (2018). Technology integration in the mobile communication industry: A review. Prabandhan: Indian Journal of Management, 11(4), 7–26. https://doi.org/10.17010/pijom/2018/v11i4/122824
17) Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence. Internet Policy Review, 9(2), 1–16. https://doi.org/10.14763/2020.2.1469
18) Mahadevan, K., & Joshi, S. (2021). Trends in electronic word of mouth research: A bibliometric review and analysis. Indian Journal of Marketing, 51(4), 8–26. https://doi.org/10.17010/ijom/2021/v51/i4/158468
19) Mahajan, Y., & Gadekar, A. (2021). A bibliometric analysis of buzz marketing: Research areas, concerns, and suggestions for advancement. Indian Journal of Marketing, 51(2), 43–59. https://doi.org/10.17010/ijom/2021/v51/i2/157550
20) McKay, C. (2019). Predicting risk in criminal procedure: Actuarial tools, algorithms, AI, and judicial decision-making. SSRN. https://doi.org/10.2139/ssrn.3494076
21) Mohapatra, A. K., Matta, R., Soni, R., & Hiremath, N. V. (2024). Evaluating the role of artificial intelligence on ESG reporting: Evidence from India. Prabandhan: Indian Journal of Management, 17(11), 8–22. https://doi.org/10.17010/pijom/2024/v17i11/174020
22) Moore, T. R. (2019). The upgraded lawyer: Modern technology and its impact on the legal profession. University of the District of Columbia Law Review, 21. https://digitalcommons.law.udc.edu/udclr/vol21/iss1/4
23) Nhemi, S. (2023). Law without lawyers: Examining the limitations of consumer-centric legal tech services. Journal of Intellectual Property and Information Technology Law, 3(1), 15–76. https://doi.org/10.52907/jipit.v3i1.223
24) Polisetty, A., & Sheela, P. (2023). Will AI replace humans in human resources? A case analysis. Prabandhan: Indian Journal of Management, 16(6), 25–38. https://doi.org/10.17010/pijom/2023/v16i6/172862
25) Rodrigues, R. (2020). Legal and human rights issues of AI: Gaps, challenges, and vulnerabilities. Journal of Responsible Technology, 4, Article ID 100005. https://doi.org/10.1016/j.jrt.2020.100005
26) Schmidt, M., & Lipson, H. (2009). Distilling free-form natural laws from experimental data. Science, 324(5923), 81–85. https://doi.org/10.1126/science.1165893
27) Siddiqui, A., Siddiqui, M., & Kulkarni, N. (2022). Artificial intelligence in water conservation: A meta-analysis study. Prabandhan: Indian Journal of Management, 15(3), 24–41. https://doi.org/10.17010/pijom/2022/v15i3/160407
28) Singh, N., Jain, M., Kamal, M. M., Bodhi, R., & Gupta, B. (2024). Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory. Technological Forecasting and Social Change, 198, Article ID 122967. https://doi.org/10.1016/j.techfore.2023.122967
29) Thanaraj, A., & Gledhill, K. (2023). Teaching legal education in the digital age: Pedagogical practices to digitally empower law graduates (1st ed.). Routledge.
30) Ubgade, P. N., & Joshi, S. (2022). A review of brand anthropomorphism: Analysis of trends and research. Prabandhan: Indian Journal of Management, 15(10), 47–62. https://doi.org/10.17010/pijom/2022/v15i10/172408
31) van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
32) Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
33) Zekos, G. I. (2021). AI and legal issues. In Economics and law of artificial intelligence: Finance, economic impacts, risk management and governance (pp. 401–460). Springer. https://doi.org/10.1007/978-3-030-64254-9_10
34) Zharova, A. K. (2023). Achieving algorithmic transparency and managing risks of data security when making decisions without human interference: Legal approaches. Journal of Digital Technologies and Law, 1(4), 973–993. https://doi.org/10.21202/jdtl.2023.42
35) Zupic, I., & Čater, T. (2014). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629