Future-Oriented Civil and Private Law: Integrating Artificial Intelligence (AI) and Machine Learning (ML) Technologies

This study was based on a very healthy three-stage methodology that asked the AI and ML in civil and private law. The first level began to analyze the performance, from which the most famous scientists who shaped the fields through their leading contributions in the literature were introduced. The second level was a citation analysis, which provided a more detailed view of the influence controlled by different research entities such as publications, authors and even countries. Finally, we performed a network analysis using advanced software such as VosViewer and Rstudio with Bibliometix package. This network analysis consisted of coexistence of keywords, factorial analysis, trends analysis, co-authorship network and bibliographic bonds. According to Van Ecka and Waltman (2010, 2017), such tools have made it possible to create the order of the most important contributors to the field. Bibliometric tests were carried out using a friendly interface offered by Bibliometrix Rstudio (Van Eck and Waltman 2010, 2011, 2013, 2017; Waltman et al., 2010). This research therefore took advantage of the combination of established bibliometric techniques and advanced software as tools to understand the intellectual landscape within AI and ml in civil and private law.

A surge of interest has built up in recent scholarship on the integration of AI and ML into civil and private law. In this respect, Table 1 reflects both the WoS and Scopus databases for the period 2017–2023 as shown in table 2. In this analysis, 279 publications from 45 sources were used and included prestigious journals such as Artificial Intelligence and Law and University of Pennsylvania Law Review. Despite this growing interest, the annual growth rate is negative at −18.42%, with 3.12 years being the average age of the documents. These are a set of varied publications written by 195 different authors. The key focuses are: deep learning applied to legal cases, scalability of legal prediction models, ethics of the deployment of AI, and civil and private law from guidelines of AI ethics. The average number of citations per document is 6.34, indicating how important this set of documents is to the scholarly community.

Figure 1 further supports the emerging interest in incorporating AI and ML into civil and private law. The literature around this topic has increased considerably over recent years, as represented by the graphic. In 2023, there were 55 major publications, which demonstrated the field’s strong growth. Representative works for this year include Bhattacharya et al. (2023), which researched deep learning techniques on the analysis of legal documents; Branting et al. (2021), which researched scalable and explainable legal prediction models; and Chalkidis & Kampas (2019), which provided early discussions on AI and ML adoption in legal word embeddings. The upward trajectory is crystal clear: from just seven publications in 2018 to 53 in 2022, this evidences that the research activity kept increasing over the last couple of years.

Fig. 1:

Development of corporate civil and private law literature using artificial intelligence and machine learning.

Figure 2 presents a view of those countries that are most productive and highly influential in terms of AI, ML, and law research. The USA, with an overall output of 44 publications and 137 citations, heads the list. Closely following this ranking are the Netherlands with 21 publications and 28 citations, Canada with 19 publications and six citations, and the UK with 17 publications and 78 citations. The most active countries are Germany and Poland, with 16 and 14 publications, respectively. Italy and China stand out for their impact with 13 and 12 publications, respectively. Russia and Ukraine complete the list.

Fig. 2:

The most productive and impactful countries.

Analysis of the publications shows different levels of international collaboration in research on AI and law. Although the USA leads with 23 articles, 95.7% of these publications are single-country papers (SCPs).. It represents a very small portion (4.3%) of Multi-Country Papers (MCPs) (publications with researchers from other countries) as shown in figure 3. Similar is the case with Germany and UK in that UK stands higher at 55.6% of international collaboration through MCPs. Furthermore, valuable contributions come from the Netherlands, Russia, Canada, and China, all at different levels of international collaboration in their respective outputs. Even smaller contributors like Poland, Ukraine, and Australia show interest in this area, further indicating the trend of global interest in research at the intersection of AI and law.

Fig. 3:

Publishers associated with the corresponding country’s research output.

Figure 4 shows the trends of publication and citation activity of influential authors in this area over time. The circle size represents annual publication rates. Larger circles represent a researcher who published more that year. Darker circle colors indicate higher citation rates for the representative publications. These are, among others, Branting (2021) with a high citation rate: 52 citations (total citations per year: 13) and Bench-Capon (2022), a very productive author with several publications: five papers, 20 citations, and an average of 6.667 citations per year. This enables us to pinpoint important contributions such as Chalkidis and Kampas (2019) on deep learning applications in law and Branting et al. (2021) on scalable legal predictions. Influential works by Bench-Capon (2017) and Verheij (2020) also point out the evolution of the integration of AI into the legal frameworks. Data to that effect reinforces the increasing impact of AI in both legal scholarship and practice.

Fig. 4:

The publication and citation rates of the top authors over time.

Figure 5 and Appendix I present the most cited papers on work that deals with how AI and ML are changing civil and private law. Overall, 140 publications, representing data from 2017 to 2023, address topics such as legal word embeddings (Chalkidis & Kampas, 2019) and scalable legal prediction models (Branting et al., 2021). The USA dominates this research landscape, but the UK leads in international collaboration, with 55.6% of its publications involving multiple countries. The leading authors are Branting (2021) based on citations, while Bench-Capon (2022) is more prolific. Influential works explore AI’s role in legal practice – Engstrom (2021), Verheij (2020) – and grapple with ethical considerations: Simshaw (2018), Larsson (2020). The research deals with the steadily high influence of AI on civil and private law, defines further research into its potential, and points out ethical consequences.

Fig. 5:

Citation analysis.

This section focuses on the network of relationships among researchers in the areas of AI and civil and private law. The frequency at which researchers cite others characterizes the domains of cooperation and intellectual interchanging. Figure 6 shows a mapping of such co-citation patterns. The more interconnected the sources, the closer the link between the scholars who published them. Out of 3,837 sources examined, 12 significant links were identified to show a high level of interconnectedness among journals within this setting. This was in agreement with our earlier observations of good citation practices within the scholarly community. It demonstrated an established network, wherein researchers cite and build on other people’s research.

Fig. 6:

Co-citation patterns.

We extend our analysis to collaborative patterns of scholars in this area of AI and civil and private law. Figure 7 shows the co-authorship pattern and displays only 16 links for the 185 authors identified; this would indicate a limited collaboration degree among researchers. This could become one of the viable ways of future development: increasing the level of collaboration and knowledge flow between the institutions active in this type of research. In addition, further collaboration can lead to more in-depth and influential studies on AI and ML integration into civil and private law.

Fig. 7:

Top-cited co-authorship patterns.

Our analysis also explored collaborative patterns between countries in AI and civil and private law research (Figure 8). This revealed some particularly strong research partnerships. Notably, the USA, England, and Luxembourg appear to be collaborating extensively on projects that explore AI and ML applications in civil and private law (Aladayleh et al., 2023). Furthermore, there are also signs of German–Polish collaboration and the involvement of scholars based in the USA with this joint effort. This illustrates well that the challenges and opportunities arising from AI and ML for the area of civil and private law are indeed global.

Fig. 8.

Distribution map.

We used VOSviewer software to analyze the keywords involved in research – a powerful network analysis tool that confirmed some of the selections we had initially made as shown in table 3. Figure 9 shows that there are clear clusters apparent in the research landscape, mapping key themes. The purple cluster is the most significant and includes some of the pivotal topics such as ML, AI, and law. Broader themes in corporate governance include legal technology, legal reasoning, and natural language processing, which also connect with those core concepts. Other applications, such as time series prediction and clustering, further show the practical integration of AI and ML. Curiously, algorithmic trading forms a separate cluster, indicating that research in this field was focused at an earlier stage than for other topics examined in the present study. Overall, clustering analysis underlines that AI and ML methodologies are finding their role in civil and private law, which is increasingly applied and integrated into different legal contexts.

Fig. 9:

Keyword co-occurrence.

Figure 10 presents the emerging frontiers in corporate governance research. Yellow highlighted areas represent newly explored themes, demonstrating how interest in sustainability and autonomy has grown in this area. This also describes a trend observed in most other aspects of modern civil and private law practices due to the pace of influence set by AI/ML. The figure illustrates the changing landscape where AI/ML is increasingly becoming embedded and important in the development of new approaches to corporate governance.

Fig. 10:

Visualizing keyword overlap.

Our analysis extends beyond the typical two-dimensional visualizations. Figure 11 provides a one-of-a-kind three-dimensional perspective on 20 pivotal sources in this field. This view allows us to examine the relationships between journals (positioned on the left axis), the countries where the research originates (middle axis), and the keywords used in the publications (right axis). This visualization reveals the USA, Canada, and the Netherlands as leading contributors to research in AI and civil and private law. Interestingly, “Artificial Intelligence and Law” emerges as the most frequently referenced publication, solidifying its position as a highly influential journal in the field.

Fig. 11:

Trilateral visualization.

The chosen cluster titles effectively capture the essence of each research theme within the identified groups. These concise titles act as summaries of the dominant topics, offering a clear and focused lens through which to understand the content of each research area. By analyzing the keywords that frequently appear together (co-occurrence), researchers were able to group related studies and assign them descriptive titles that reflect their core themes.

The rapidly growing area of civil and private law has seen great developments, but significant hurdles persist in the attempt to integrate AI and ML. Deep learning techniques and extensive legal databases have allowed recent improvements in legal prediction models; these have been implemented by developing legal word embeddings (Chalkidis & Kampas, 2019). Still, making those models scalable and explainable remains a long-standing challenge. Branting et al. (2021) raise the need for strong frameworks that ensure accuracy and transparency in legal decision-making. Ethical issues are still high, with debates on the ethical effects of AI-powered legal practices. These further underscore the calls for clear guidelines and mechanisms for oversight (Simshaw, 2018). In this respect, governance and regulatory frameworks are quite essential to the effect that AI imposes on the conducts of legal proceedings. In this respect, Larsson (2020) explains that ethical principles guide AI applications in civil and private laws to maintain a proper balance between innovation and accountability. Verheij (2020) discusses how AI shapes legal norms and how frameworks should ensure that technological advances in such technologies bely within set legal principles. Despite these efforts, integrating AI into the application of civil and private law practices effectively remains challenging to achieve in practice (Engstrom, 2021). Refining and validating AI systems for legal use is difficult due to complexities in interpreting legal texts and categorizing legal norms with AI models (Waltl et al., 2019; Tagarelli & Simeri, 2022).

Cluster 2 represents a dynamic area on the penetration of artificial intelligence and civil/private law, but also represents a large number of challenges. Research emphasizes progress in the use of AI for legal forecasts and arguments using techniques such as deep learning and insertion of a legal word (Chalkidis & Kampas, 2019), but there is a question about the scalability and explaining of these models. Branting et al. (2021) emphasize that robust frameworks are needed to ensure accuracy and transparency in decision-making in the environment of civil and private law. Ethical considerations are no less important because the introduction of AI into legal practice depends on well-defined leadership and supervision to avoid bias and to ensure justice (Simshaw, 2018; Abdo et al., 2023; Abdo et al., 2021; alqudah et al. Management and regulatory framework play a key role, and scientists advocate the development of ethical instructions to effectively control AI applications (Larsson, 2020). In addition, the interpretation of legal texts and classification of legal standards using AI models (Waltl et al., 2019; Tagarelli & simeri, 2022) represents challenges that require further improvement and verification. This leaves a larger gap in understanding the wider result of AI on legal arguments in civil and private law. Further research will be required to develop holistic frames that align technological progress to established legal principles (Engstrom, 2021; Verheij, 2020).

Cluster 3 emphasizes critical gaps in the field of research and orientation, reflecting the developing relationship between AI and private law. The scholars examined the Framework of Administration for AI (Larsson, 2020; Engstrom, 2021), emphasizing ethical instructions and regulatory structures in civil and private law. Research of scalable and explained legal prediction models (Branting et al., 2021; Gowder, 2018) increases transparency in the legal decision-making of controlled AI. In addition, deep learning application (Chalkidis & Kampas, 2019; Qudah et al., 2021; Wang, 2020) improve legal text analysis and predictive analysis. Ethical concerns remain central, especially in terms of legal decisions and responsibility controlled by AI (Simshaw, 2018). The discussion of the civic legal personality AI (Ziemianin, 2021; Qudah et al., 2021; Garingan & Pickard, 2021) question traditional legal frameworks and question the status and rights of autonomous systems. The impact of AI applies to family law (Gingras & Morrison, 2021), while predictive analysts form legal administration (Lazaro & Riszi, 2023). In addition to the AI family law, the Consumer Protection Act and the Act on Mental Property significantly affect. AI controlled client services and algorithmic decisions are evoked by concerns about the rights and transparency of consumers (Micklitz et al., 2022). In the Mental Property Act, the AI-generated content is questioned by traditional copyrights, especially in Europe, where legal debates continue over the potential AI (Hilty & Richter, 2023). Transformative AI roles in private law require interdisciplinary cooperation between legal scientists, politicians and AI scientists. Recent legislative development, in particular the Act on Artificial Intelligence (Regulation (EU) 2024/1689), further emphasizes the need for legal adaptation. This regulation provides harmonized rules for AI, affecting several private legal domains and forming an ongoing legal discourse on AI administration.

Cluster 4 reveals crucial research gaps and future directions on the current debate of integrating AI into civil and private law. As much as AI may be an assured pointer to bright future developments in legal contexts, there are also ethical and operational challenges unique to civil and private law (Simshaw, 2018; He, 2019). Pioneering research has identified deep learning, in terms of adapting legal word embeddings previously trained on vast datasets, as a means to enhance decision-making in civil and private law (Chalkidis & Kampas, 2019). This seminal work further emphasizes the need for strong structures to ensure equity and accountability within AI-powered legal systems, with particular respect to civil and private law. In addition, legal prediction models, scalable and explainable, A feature highlighted by Branting et al. (2021) is the importance of adding plausibility to AI decisions, which is essential for upholding the rule of law and promoting ethical conduct in civil and private legal practices. Ethical issues concerning the use of AI in legal practice by Simshaw (2018) are paramount and demand the creation of guidelines that shall govern its use and protect legal integrity. Innovative approaches such as unsupervised law article mining (Tagarelli & Simeri, 2022) offer new avenues for efficiently extracting and utilizing legal knowledge, enhancing legal technologies’ persuasive capabilities within civil and private law. These approaches also necessitate interdisciplinary collaboration among legal scholars, AI experts, ethicists, and policymakers. Such collaboration is crucial to navigating the complexities of effectively integrating AI into civil and private law.

The purple cluster covers an up-and-coming area in which the latest developments in AI are changing the practices of the legal profession, but simultaneously and importantly points to some lacuna that are worthy of further investigation. The early works focused on the adaptation of deep learning techniques to legal contexts, training legal word embeddings on large legal databases (Chalkidis & Kampas, 2019; Kowalski & Datoo, 2022). These innovations represent the potential to identify valuable insights from datasets and enhance processes of legal decision-making. However, scalability and explainability of AI-driven legal prediction models remain among the most challenging issues for the time being (Atkinson et al., 2020). Transparence and interpretability of these models constitute an important requirement of legal compliance and ethical standards within judicial systems (Branting et al., 2021; Künnapas, 2021). Governance frameworks and ethical guidelines are also key to the successful integration of AI into the realm of law. Scholars argue for strong ethical guidelines for the responsible deployment of the emerging technologies AI represents. These guides must take into consideration pressing issues on fairness, accountability, and integrity within the legal order (Larsson, 2020; Simshaw, 2018). Meanwhile, unsupervised mining of law articles constitutes but one example of emerging capabilities related to automating and optimizing legal knowledge. However, overcoming technical and regulatory hurdles necessitates interdisciplinary collaboration between legal scholars, AI experts, ethicists, and policymakers (Tagarelli & Simeri, 2022; Engstrom, 2021).

Light Blue Cluster: This is a field that has witnessed several great success in applying deep learning techniques in legal contexts. Pioneering works, such as Chalkidis and Kampas (2019), focused their efforts on developing legal words that were trained on the basis of relatively large data sets. This pioneering effort has laid the foundations for adapting the deep learning techniques to analyze legal texts, as discussed by Francesconi (2022) and Barys lit (2022). However, the fields still show critical gaps and challenges that require further investigation in Chatziathanasia (2022). How Branting et al. (2021) Note, scalability and interpretability remain key concerns in legal AI. Scalable and interpreted legal prediction models are of fundamental importance for ensuring practical AI applications in legal decisions. This requires overcoming technical difficulties without trading in the AI transparency and responsibility. An ethical consideration is another very important aspect presented by Larsson (2020) and Simshaw (2018). Strong ethical frames should be baked in the development and use of AI in legal systems for justice, responsibility and transparency. Indeed, such framework provides instructions for ethical application of AI; They also work in public and gain more confidence in various AI legal applications. In addition, interdisciplinary cooperation - as Tagarelli and Simeri (2022) emphasized - moreover, bridge the gap between technological progress and regulatory framework. It can also articulate regulatory challenges and gently fine-tune the AI models so that models more efficiently process legal complexity and find innovative applications to strengthen legal practice in terms of efficiency and justice.

The orange cluster deals with the exciting area of AI and civil and private law. This area seeks to transform legal procedures by using technological innovations to improve the practice of law and decision-making. As such, Mandal et al. (2022) and Governor et al. (2022) noted that significant progress was made, especially pioneers in the techniques of deep learning and the words insertion of the law (Chalkidis and Kampas, 2019). This timely development made it possible to analyze and interpret legal texts powered by artificial intelligence, which gives a significant change in the milestone in civil and private law technology. However, there are still challenges: for example, Branting et al. (2021) point out that the legal forecast needs AI models that are scalable and explained. This is necessary to ensure transparency and reliability within the process of civil and private legal decisions. In addition, Bench-Capon (2017) and Larsson (2020) emphasize the importance of administration frames and ethical instructions in terms of AI deployment in a legal context. This ongoing debate signals the need to balance technological progress with ethical considerations of civil and private law. Simshaw (2018) expresses ethical interest in legal artificial intelligence, including requirements for strong frameworks that adhere to justice and responsibility in the processes of the law. Verheij (2020) and Rigoni (2018) argue for the integration of AI into established legal principles of civil and private law. This is a key step towards harmonizing technological progress with the main legal standards.

This study builds on the works of Khan et al. (2020) and Paltrinieri et al. (2023), determination of the importance of bibliometric and content analysis with regard to the identification of emerging trends from this very dynamic area of AI and ML in civil and private law. These research questions, as shown in Table 4, are an important contribution to scientific debate and expand the reach beyond what AI/ml/ml applications are currently being discussed in legal contexts. Research supports critical questions about future research directions, requires responsible investigation and measurement procedures, and visit thresholds in this area.

This research also serves to identify persistent research gaps that require further investigation. Particularly, the integration of AI/ML into legal practices, including the challenges of scalability, transparency, and ethical concerns, forms a general theme underlying many clusters of research, such as in legal natural language deep learning in Cluster 6, legal prediction models in Cluster 3, and the use of AI in governance frameworks in Cluster 5. These areas indicate the need for both interdisciplinary collaborations to ensure that the development of technologies is in line with ethical guidance, legal standards, and socially acceptable values. These findings are set to inform future studies on AI and the law and will encourage the development of stronger and more scalable legal prediction models. By engaging with the issues arising from uncertainty regarding the impact AI/ML will have on contemporary civil and private law, this research lays the foundation for more informed decision-making processes, better regulatory regimes, and a more profound insight into challenges the legal profession as well as society as such will face in the future. Through these efforts, scholars will be better prepared to contribute to the effective integration of AI into legal systems while maintaining fairness, accountability, and transparency.

Research of European technologies of civil and private law, especially the AI and ML-based ones, is doing well. Deep learning techniques have contributed to this development by being used for legal applications. For example, inserting legal words significantly improved the performance of legal forecasts (Chalkidis & Kampas, 2019). This is the basis for a deeper analysis and interpretation of the content of legal texts to further increase efficiency and efficiency in the decision-making process in European legal systems. Calls are constantly published in European and private law in terms of scalability and explaining the AI models. Other scholars such as Branting et al. (2021), emphasize that strong structures must be introduced for AI systems in order for AI systems not only accurate but also transparent, throwing light on some distortion and ethical consequences resulting from AI into legal practice. Ethical considerations also focus on the need for clear administration and regulatory framework, especially in the European context, to manage the responsible use of AI technologies in civil and private law. This is a shared Simshaw (2018) view.

The recently adopted Act on Artificial Intelligence, Regulation (EU) 2024/1689, is helpful in determining harmonized rules for AI throughout the EU, and therefore has consequences for private law. It is a regulation that has undergone a very long process of legislation, reflecting increased awareness of the need for comprehensive AI management. Its impact is huge in private law because it provides a regulatory framework for the responsible use of AI systems in the legal area. The law deals with questions such as transparency, responsibility and safety that are critical when AI technologies are applied in legal contexts. More structured approaches to the integration of AI into European legal systems, in particular those that ensure compliance with stipulated standards and principles in private law, appear from a more detailed examination of the impact of the law on literature.

Bedding gaps in AI/ml research and practical legal applications throughout Europe will require considerable efforts. Scientists have noticed the potential transformation effect of AI technology on legal administration and decision-making (Engstrom, 2021). However, trouble-free AI integration into European legal systems faces practical challenges, such as how AI models can interpret legal texts and classify legal norms specific to European civil and private law (Waltl et al., 2019; Tagarelli & simeri, 2022). Understanding the nuances of legal texts across various jurisdictions, languages and legal traditions represent another layer of complexity. Future research should focus on improving AI systems to deal with the complexity of legal matters and at the same time to reconcile European legal principles and frames (Verheij, 2020). This research agenda should be formed by examining the social and organizational effects that AI adopts within European civil and private law. Thus, interdisciplinary cooperation between the sector of legal, AI and political sectors will be decisive in the proceedings of innovation to create justice and efficiency of European legal practices in the AI legal landscape.

The future of AI and ML in European civil and private law is very promising. Among the key trends you can notice the development of more interpreted and scalated models of legal forecasts of controlled AI. Branting et al. (2021) show how further improvements in deep learning methods, especially those that have a legal word and natural language processing, provide even more opportunities for such models. Research must also be directed to the development of complex ethical instructions for the use of AI in European law practices to ensure that such integration is non-discriminatory and responsible, as suggested by Larsson (2020). In addition, the legal environment in Europe, including complicated rules, such as the general data protection regulation or GDPR, should also be taken into account in the application of AI in civil and private law. Therefore, any AI application will have to meet local and Pan-Eu standards of privacy. Finally, highly complex problems require interdisciplinary cooperation between lawyers, scientists AI, ethicists and politicians. Optimization of AI within European civil and private law requires innovative solutions to streamline the procedures and at the same time to maintain the integrity of legal systems in Europe before a demanding landscape of developing technology.