Future-Oriented Civil and Private Law: Integrating Artificial Intelligence (AI) and Machine Learning (ML) Technologies
17 jun 2025
Acerca de este artículo
Publicado en línea: 17 jun 2025
Páginas: 38 - 58
Recibido: 27 sept 2024
Aceptado: 09 feb 2025
DOI: https://doi.org/10.2478/cejpp-2025-0004
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© 2025 Atef Salem Alawamleh, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Query for the Ultimate Data Compilation_
| All = ((“machine learning” OR “deep learning” OR “support vector machine” OR “artificial neural network” OR “supervised learning” OR “random forest” OR “reinforcement learning” OR “text processing” OR “AI” OR “ML” OR “artificial intelligence”) AND (“civil and private law” OR “private law” OR “legal technology” OR “AI in law” OR “legal machine learning” OR “law automation” OR “legal AI applications” OR “legal analytics” OR “AI in judicial systems” OR “AI in legal research” OR “future law technologies”)) | 841 publications initially identified, restricted to online English sources in accounting and finance |
| Restrict the investigation to articles published from 2017 to 2023 and include journals indexed in SSCI, SCI-EXPANDED, or ESCI | Reduced to 387 articles after refining the timeframe and focusing on indexed journals |
| Remove publications unrelated to accounting and finance through manual screening to ensure a comprehensive study |
To ensure a comprehensive study focused on the intersection of accounting and finance, a manual screening of the initial dataset was conducted to remove publications unrelated to the field. After this rigorous screening process, the final dataset comprised 279 relevant publications These publications were divided as follows: 140 articles from the WoS database and 139 articles from the Scopus database |
Key Insights, New Directions, and Bridging Gaps_
| Red | Deep learning and legal word embeddings for legal prediction | Scalability and explainability of AI models, ethical considerations, and need for clear guidelines | Limited research on integrating AI with legal reasoning and argumentation, lack of studies on the impact of AI on access to justice | Refining AI models for civil and private law applications, enhancing interpretability and robustness, developing ethical frameworks |
| Green | Similar findings to Red regarding deep learning and legal embeddings | Similar challenges to Red regarding scalability, explainability, ethics, and governance | Need for research on generating persuasive legal arguments, limited understanding of potential biases in AI-driven legal argumentation | Similar future research directions to Red, understanding broader impact of AI on legal argumentation, methods for evaluating persuasiveness of AI-generated legal arguments |
| Blue | Importance of governance frameworks for AI in legal contexts | Need for scalable and explainable legal prediction models, ethical considerations surrounding AI in legal professions | Gap in research on the impact of AI on the legal profession, lack of studies on legal implications of autonomous AI systems | Interdisciplinary collaboration for navigating AI integration in civil and private law, research on AI’s civil legal personality and its legal implications, exploring AI for family law and predictive analytics in legal governance, incorporation of AI in consumer protection law, addressing challenges related to consumer rights, contracts, and dispute resolution, examination of intellectual property law, especially concerning AI authorship and intellectual property rights in the context of AI-created works (a growing debate in Europe) |
| Yellow | Deep learning for legal decision-making processes | Importance of ethical considerations and robust frameworks | Overcoming technical and regulatory hurdles for unsupervised law article mining, limited understanding of tailoring legal arguments to specific audiences | Exploring interdisciplinary collaboration for effective AI integration, methods for unsupervised legal knowledge extraction, research on personalizing legal arguments for different audiences |
| Purple | Deep learning techniques for legal text analysis | Importance of ethical guidelines and overcoming regulatory hurdles | Challenges in adapting Natural Language Processing (NLP) techniques to legal language, lack of research on AI’s impact on legal writing and communication | Developing NLP techniques for handling legal complexities, research on improving clarity and efficiency of legal writing with AI |
| Light blue | Pioneering work on legal word embeddings for deep learning analysis of legal texts | Scalability, explainability, and ethical considerations | Limited research on integrating AI with legal reasoning and case law analysis, lack of standardized datasets for training and testing AI models in civil and private law | Fostering interdisciplinary collaborations for regulatory framework development, enhancing scalability and interpretability in legal AI models |
Top Citations on Artificial Intelligence and Machine Learning in Civil and private law_
| Chalkidis & Kampas | Deep learning in law: early adaptation and legal word embeddings trained on large corpora | 2019 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 27 | 2 | 171 | 198 | 27 | 79 | National Kapodistrian University of Athens | Greece | Springer |
| Branting et al. | Scalable and explainable legal prediction | 2021 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 29 | - | 213 | 238 | 25 | 52 | MITRE Corporation | USA | Springer |
| Bench-Capon | HYPO’s legacy: introduction to the virtual special issue | 2017 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 25 | - | 205 | 250 | 45 | 37 | University of Liverpool | UK | Springer |
| Larsson | On the governance of artificial intelligence through ethics guidelines | 2020 | Asian Journal of Law and Society | 2052–9015 | 0.873 | 7 | 3 | 437 | 451 | 14 | 35 | Lund University | Sweden | Cambridge University Press |
| Engstrom | Digital Civil Procedure | 2021 | University of Pennsylvania Law Review | 0041–9907 | 2.107 | 169 | 8 | 2243 | 2286 | 43 | 24 | Stanford University | USA | University of Pennsylvania |
| Verheij | Artificial intelligence as law: Presidential address to the seventeenth international conference on AI and law | 2020 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 28 | 2 | 181 | 206 | 25 | 24 | University of Groningen | The Netherlands | Springer |
| Rigoni | Representing dimensions within the reason model of precedent | 2018 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 26 | - | 1 | 22 | 21 | 23 | European University Institute | Italy | Springer |
| Tagarelli & Simeri | Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code | 2022 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 30 | 3 | 417 | 473 | 56 | 20 | University of Calabria | Italy | Springer |
| Simshaw | Ethical issues in robo-lawyering: The need for guidance on developing and using artificial intelligence in the practice of law | 2018 | Hastings Law Journal | 0017–8322 | 1.25 | 70 | - | 173 | 211 | 38 | 16 | University of California | USA | University of California |
| Waltl et al. | Semantic types of legal norms in German laws: classification and analysis using local linear explanations | 2019 | Artificial Intelligence and Law | 0924–8463 | 1.233 | 27 | 1 | 43 | 71 | 28 | 16 | Technical University of Munich | Germany | Springer |
Most Used Keywords_
| 1 | Artificial intelligence | Blue | 23 | 17 |
| 2 | Law | Blue | 36 | 11 |
| 3 | Argumentation | Green | 33 | 7 |
| 4 | Legal | Light Blue | 20 | 7 |
| 5 | Machine learning | Purple | 22 | 7 |
| 6 | AI and law | red | 20 | 6 |
| 7 | Big data | Blue | 10 | 6 |
| 8 | Legal technology | Blue | 8 | 6 |
| 9 | Legal reasoning | red | 20 | 5 |
| 10 | Natural language processing | Light Blue | 12 | 5 |
| 11 | AI | red | 7 | 4 |
| 12 | Knowledge | Green | 21 | 4 |
| 13 | Artificial intelligence and law | Purple | 5 | 3 |
| 14 | Persuasion | Yellow | 12 | 3 |
| 15 | Proof | Orange | 13 | 3 |
Key Data from the WoS and Scopus Databases_
| 2017–2023 | The timeframe during which the data was collected and analyzed | |
| 45 | The number of different sources from which the documents were obtained, including journals, books, and other publications | |
| 279 | The total number of documents analyzed in the study, including articles, book chapters, early access papers, and reviews | |
| −18.42 | The annual growth rate percentage indicates a decrease in the number of documents over time, expressed as a negative value | |
| 3.12 | The average age of the documents in years, indicating how recently they were published | |
| 6.34 | The average number of citations each document received, providing insight into their impact and influence within the scholarly community | |
| 5890 | The total number of references cited across all documents, demonstrating the breadth of sources consulted and referenced in the study | |
| 135 | The number of unique additional keywords or terms identified beyond the author’s keywords, providing additional context or specificity to the documents | |
| 310 | The number of unique keywords or terms provided by the authors themselves to describe the content of their documents | |
| 195 | The total number of unique authors contributing to the documents analyzed in the study | |
| 40 | The number of authors who contributed to documents that were single authored, indicating individual scholarly contributions | |
| 55 | The number of documents that were authored by a single author, excluding co-authored works | |
| 2.5 | The average number of co-authors per document, representing collaborative efforts in scholarly writing | |
| 23.12 | The percentage of co-authorships involving authors from different countries, showcasing international collaboration in research | |
| 149 | The number of documents categorized as articles, typically representing original research or analysis | |
| 50 | The number of documents categorized as both articles and book chapters, indicating dual publication formats | |
| 10 | The number of documents categorized as articles available through early access, allowing readers to access content before formal publication | |
| 15 | The number of documents categorized as reviews, providing critical assessments of existing literature or research in the field | |