Revista Ítalo-Española de Derecho Procesal
pp. 121-142
Madrid, 2023
DOI: 10.37417/rivitsproc/1443
Marcial Pons Ediciones Jurídicas y Sociales
© Francesca Ferrari
© Davide Mustari
ISSN: 2605-5244
Recibido: 20/03/2023 | Aceptado: 28/06/2023
Editado bajo licencia Creative Commons Attribution 4.0 International License.

The New Technologies and the Civil Justice as Commons*

Francesca Ferrari

Professor at the University of Insubria

Davide Mustari

Ph.D. candidate at the University of Insubria

ABSTRACT: Civil justice certainly belongs to the “commons” and, therefore, systems need to be adopted to prevent its depletion.
New technologies and artificial intelligence tools can help prevent waste of this important resource and enable the creation of a secure and robust repository for civil legal data that will enable stakeholders, researchers and the public to better understand the system and individuals to better evaluate alternatives.
To date, however, data on civil proceedings are very scarce, and to classify them it is essential to overcome some particularly important privacy issues.
A good project from this point of view has been initiated in Italy and is called the Next Generation UPP Project. The Project aims to improve the justice system in northwestern Italy through the strengthening of Judicial Offices (JPOs), technological innovation, and experimentation with new patterns of collaboration between universities and judicial offices.

KEYWORDS: artificial intelligence; civil justice; commons; privacy protection; civil process; new technologies; predictive justice.

SUMMARY: 1. DEFINITION OF COMMONS.—2. CIVIL JUSTICE AS COMMONS.—3. PREDICTIVE JUSTICE.—4. NEW TECHNOLOGIES AND THEIR USE IN CIVIL JUSTICE VS. PRIVACY ISSUES.—5. ARTIFICIAL INTELLIGENCE IN THE EVIDENCE PHASE OF CIVIL PROCEEDINGS.—6. ACTUAL USES OF ARTIFICIAL INTELLIGENCE IN ITALIAN CIVIL PROCEDURE SYSTEM.—7. CONCLUSIONS: A RECENT ITALIAN INITIATIVE FOR NEW TECHNOLOGIES IN CIVIL JUSTICE (NEXT GENERATION UPP).

1. Definition of commons

It is our conviction that civil justice can be included in the category of the so–called “common goods”.

Before illustrating the elements that allow us such a consideration, we consider it useful to identify—albeit briefly—the elements characterizing the commons 1.

It should be pointed out that in our legal system there is neither a category 2 nor a unanimously agreed legal definition 3– 4 of common goods, which present «too much variability for them to be described by a set of fixed and universal principles» 5.

In any case, they can be understood as those goods, endowed with the requisites of essentiality and irreplaceability 6 that acquire their characterization due to the constraint of destination that marks them, being functional to the realization of the fundamental rights of the person 7 and generating a collective utility 8.

Indeed, it is precisely the material and socio–cultural needs of the person that lead to the identification of that goods that, in the name of the selfish nature of the human beings, cannot be left to the government of mercantile logic. Moreover, in relation to these goods, it is necessary to adopt measures that can guarantee their indiscriminate accessibility to all, as well as their correct and efficient use.

In particular, considering the typical exhaustible character of common goods, it is necessary to ensure their exploitation in proportion to collective needs and the capacity of individuals, avoiding excess and waste and ensuring their efficient use.

Emblematic on this point is the essay by the American ecologist Garret Hardin entitled “The Tragedy of the Commons” published in the journal Science, in which context he argued that free enterprise in the management of the commons would lead to the ruin of all 9.

In support of this assumption, Garret recalled the position of the economist William Forster Lloyd, who believed that grazing land open to the use of anyone who wants to enjoy it has the inexorable destiny of promoting a crisis of the system and of being exhausted in view of the continuous increase of shepherds who would like to exploit it to increase their flock 10.

Hardin points out that the same reasoning can also be followed regarding pollution, since «the rational actor finds that the cost of his portion of the discharge into the common good costs less than it would cost him to clean up his discharge before disposing of it» 11.

It is precisely based on these considerations that Directive 2004/25/EC of the European Parliament and of the Council of 21 April 2004 on environmental liability concerning the prevention and remedying of environmental damage was adopted.

Ultimately, it is possible to consider as “commons” those goods from the exploitation of which no one can be excluded, devoted to be exhausted or not to work in the absence of adequate institutional interventions.

It has to be underlined that common good is not necessarily an element of nature (e.g. water, the atmosphere, etc.), but can be also the result of social production (e.g. public services). In these latter cases the non–exclusion of anyone is simply the outcome of a decision at legal or political level. In this respect we firmly believe that a community having made certain decisions, can count, among its common goods, also services of public interest and in particular civil justice 12. In this case clearly the non–exclusive character is determined not by the nature of the goods, but by—as already said—a decision at political, legal or sociological level.

In the Italian Constitution some fundamental rights require the organization of specific public services: social dignity and the full development of the human person (art. 3 of the Constitution), work (art. 4), personal freedom (art. 13), health protection (art. 32), education (Art. 33 and 34), the protection of one’s rights and legitimate interests (art. 24). Civil justice is the essential tool in order to ensure the last mentioned right and access to it cannot be excluded in order to protect this right as well as many others fundamental rights provided for by this Constitution.

2. Civil justice as commons

Having reached this point, we wish to reiterate our assumption that civil jurisdiction is a “commons”.

What is clear is the benefit that the community obtains from a well–functioning civil justice system, which is aimed at the correct application of legal norms and guaranteeing the protection of rights.

On the other hand, even limiting the analysis to business matters, it cannot be disputed that the defense of property rights and the enforcement of compulsory relationships are essential elements for the proper functioning of the national economy. Indeed, in an economic system in which the jurisdictional protection of property rights is effective, «the incentives to save, to invest and to start new business activities and to expand existing ones are, ceteris paribus, greater, with positive repercussions on the country’s medium– to long–term growth prospects» 13.

That said, it must be acknowledged that the “resource of justice”, unfortunately, is not an unlimited resource. In this regard, there is a need for institutional intervention that not only guarantees indiscriminate access to it to all those who need it, but also regulates its exploitation to prevent the so–called “tragedy of the commons”.

In this respect, it is well known that new technologies and specifically artificial intelligence tools, can play a fundamental role in improving the effectiveness of civil justice.

It is therefore mandatory the creation of a secure and robust archive containing civil judicial data, which could be instrumental in making research faster and less costly for legal practitioners in general but, above all, for “those who have the last word”, the judges. Such an archive, if accessible to individual citizens, would allow for greater knowledge of living law with the consequent possibility of self–determination by being able, at the very least, to foresee the resolution of judicial disputes in relation to similar cases.

The subject is particularly complicated if one considers that there are difficulties in defining even the term “artificial intelligence” 14. In very general terms, it can be said that it is the science that deals with how to create intelligent machines and has found, in the possibilities offered by computer science, the way to achieve this 15.

Artificial intelligence, moreover, has also been defined as the ability of a technological system to provide performance similar to that of human intelligence, i.e. the ability to solve problems or perform tasks and activities typical of human action. This presupposes, in the most advanced systems, the ability not only to automatically and autonomously process huge quantities of data and provide answers to those questions for which such systems have been programmed, but also to acquire, on the basis of special learning algorithms, the aptitude to make predictions or take decisions 16.

3. Predictive justice

Related to the topic of the use of artificial intelligence in civil justice, the fascinating subject of predictive justice comes to the fore. The latter can be defined as «the computer tool, based on a database of case law, which, with the help of sorting/sorting algorithms and (the more refined) “neural networks”, makes it possible to anticipate what the statistical probability of success in a legal dispute will be» 17.

The advantages of using artificial intelligence mechanisms are many and undeniable, also in terms of predictive justice 18.

As far as lawyers are concerned, in particular, the use of algorithms makes it easier to know, albeit in statistical–probabilistic terms, the outcome of a possible judgement and, consequently, to avoid unnecessary litigation and, possibly, to resort to alternative ways of resolving disputes with consequent deflation of litigation and saving unnecessary litigation costs.

The use of algorithms in the administration of justice would also enable the judge to resolve disputes entrusted to him more expeditiously.

Indeed, algorithms make it possible to analyze a huge amount of data (so–called Big Data) in far shorter times than it would take a human being to do so.

These new technologies would allow for a better exploitation of the resource of justice by constituting support, guidance and integration tools for the interpretative and decision–making process, that would guarantee greater efficiency in the resolution of disputes.

From another point of view, predictive justice tools would also allow for a greater degree of predictability and stability of decisions, avoiding sudden and contradictory changes in case law 19.

Even, according to some, these instruments would be functional to guarantee—besides a modernisation and simplification of access to jurisdiction—greater legal certainty 20. The latter, moreover, would also improve access to justice for citizens, who sometimes desist from going to court for fear of facing the costs of lengthy lawsuits with an uncertain outcome. In any case, it must be emphasised that these instruments are not intended to anticipate the ruling, but rather to make the parties participate in the judge’s possible reasoning 21.

4. New technologies and their use in civil justice vs. privacy issues

That being said—apart from the many issues that may arise in connection with the functioning and usefulness of the above–mentioned mechanisms, which cannot be dealt with here—it is necessary to reconcile the use of tools belonging to the sector in question with privacy principles.

Indeed, it should be pointed out that artificial intelligence systems only “feed” on and evolve through the use of huge amounts of data from different sources and generally collected for a different purpose and this, as mentioned, poses a privacy issue.

Denying access to some or all of this data in deference to privacy would necessarily weaken the artificial intelligence that learns from the data itself: in fact, if such systems are trained on a limited dataset, representative of only a small segment of the population, they will propagate a distorted and narrow viewpoint.

Moreover, data are necessary not only for artificial intelligence to reach its full potential, but also to enable it to guard against bias and prevent errors.

Artificial intelligence, in fact, like the humans who develop it, is not free of biases or errors, however, it has the potential to avoid many of the irrational errors that characterize human decision–making and to make the detection of these errors easier and more reliable. To do this, artificial intelligence tools must train on a multitude of data, especially sensitive or protected data 22. Thus, denying access to or preventing the storage of certain data will only make it more difficult to detect and remedy distortions, while denying all segments of society the full potential of the benefits of artificial intelligence.

The subject matter is a clear representation of a substantive reality in which rights do not live in isolation at all, but rather in continuous dialogue and potentially even in conflict with each other.

Moreover, the spread of artificial intelligence systems creates the opportunity to come up with interpretative approaches suitable to allow one to enjoy the benefits of advanced technologies while at the same time being able to reasonably assume that individual privacy is guaranteed, given that artificial intelligence should first and foremost “feed” on non–personal data, to the circulation of which EU Regulation 2018/1807 is dedicated.

This requires, at the legislative level but also at the level of interpretation, a balance between the right to an efficient and functional civil jurisdiction to satisfy the fundamental rights of individuals and the right to privacy.

5. Artificial intelligence in the evidence phase of civil proceedings

The same balance is also needed to exploit the potential of artificial intelligence for certain specific tasks in the civil proceedings, such as for the evidence phase.

The interaction between artificial intelligence tools and evidence phase in civil proceedings has been studied above all in the United States in relation to discovery and e–discovery. According to the amendments that technological evolution and digitization have implied for the discovery phase, causing it to deviate towards e–discovery, legal practitioners started to look for tools capable of resolving, albeit perhaps not completely, the difficulties posed by the e–discovery and found the TAR (Technology Assisted Review), which uses sophisticated algorithms to allow a computer to determine the relevance of a document 23.

The advent of the use of these tools has first of all led to a real revolution within law firms, in the context of which, in order to cope with technological difficulties, departments have been created to manage e–discovery and, when this was not possible, the work of discovery has been entrusted to external structures specialized in IT. For law firms, the very selection of a suitable provider of these services meant acquiring new skills 24.

The need for TAR also stems from the fact that the e–discovery includes much more than what was already included in the paper discovery: it involves examining not only emails and digital documents, but also documentation originating from social media such as Twitter, Facebook and Linkedin, from databases that are not necessarily online, as well as from network–connected devices such as smartphones and car “black boxes” or from smart home automation systems such as alarm systems, thermostats or virtual assistants such as Amazon Echo, given the fact that all of the above elements store potentially relevant data and documentation both locally and on the cloud.

Precisely because of this, human–led examination was no longer a viable alternative for handling e–discovery in a plurality of disputes 25.Today, the state of the art in e–discovery is, therefore, AI–assisted predictive coding.

The review process that is carried out by the TAR starts with the identification of the electronic documents to be reviewed. A team member enters the documents into software to build an analytical index. Numerous software packages are available that can apply a set of instructions and rules (“algorithms”) to a dataset. As far as algorithms are concerned, there are also a plurality of them: in fact, alongside algorithms that enable basic keyword searches, other algorithms that have been developed in the context of Natural Language Processing—by this meaning that interdisciplinary field of research encompassing not only computer science, but also linguistics—have become increasingly important. These are algorythms capable of analysing and understand natural language in a similar way, but tend to perform better, than humans who use that language in everyday life.

Natural Language Processing is capable of proceeding to lexical and phrasal semantic analysis and also of analyzing the syntactic structure of the text, associating the morphological categories related to the individual words, identifying syntactic dependencies and, therefore, relating the meaning to the context and the way the words are used. Also within the sphere of Natural Language Processing, Word Mining and Text Mining algorithms have been developed, which, on the basis of text samples and further materials provided, proceed to an analysis of the text that differs clearly from both syntactic and grammatical analysis and which is aimed at bringing out the co–occurrence and relationships existing between the words of a text, those words and the text, the words of a text with the words of another text and with other texts and between texts. Certainly today, these algorithms are the most advanced and the most recent among those used by TAR 26.

Regardless of the specific software technology and algorithm used, it is clear that all artificial intelligence systems are based, as mentioned above, on a set of documents identified ex ante and provided to the system and, from these, select the relevant documents from all those produced 27. Once the operation of selecting original documents has been accomplished, the artificial intelligence system must also be taught the criteria by which that set of documents was selected, so that the same system can apply them to much larger sets of documentation.

As already mentioned, the exponentially growing mass of data makes it possible to fulfil what the computer lacked: big data on which to “practice” and learn autonomously. It is also evident that, regardless of the function for which it is adopted, artificial intelligence has the advantage of reducing computational time 28, which cannot be compared with the past or with human processing capacity, of improving predictive capacity 29 and of reducing costs 30.

It is precisely on the basis of these advantages that the American Supreme Court jurisprudence has shown a clear tendency to consider the use of TARs permissible 31, while emphasizing the need for the parties to share the technology used and the possibility for each party to independently assess the appropriateness of using these tools.

6. Actual uses of artificial intelligence in Italian civil procedure system

Apparently in Italian civil procedural system, and particularly in the evidentiary phase of our civil trial, artificial intelligence has no place and, considering the many IT deficiencies of the Italian courts, one could say that it is difficult to imagine its use in the near future.

This, however, is not entirely true; one only must think of proceedings for the protection of trade secrets.

Certainly, this is not the place to go over the evolution of legislation on trade secrets, however, at least few words are needed. In our legal system, the industrial secret was for a long time protected through the discipline of unfair competition and this by virtue of paragraph III of Article 2598 of the Civil Code 32; it is only with the enactment of the code of industrial property and, therefore, with legislative decree no. 30 of 10 February 2005 that the secret acquires the status of a non–titled industrial property right, with respect to the violation of which a discipline is dictated in conformity with that of the other industrial property rights 33. In particular, the Industrial Property Code dedicates Articles 98 and 99 of the IP Code 34 to the secret, thus identifying both a substantive and procedural discipline of confidential business information 35. Such discipline is moreover perfectly in line with the TRIPs agreements 36. With the corrective decree of the Industrial Property Code (legislative decree no. 131 of 13 August 2010) 37 the legislator returns to deal with the institute in question and, in particular, amends Article 99 of the IPC, specifying, in the context of the above–mentioned provision, that the owner of the secret may prohibit third parties from conduct consisting in the acquisition and disclosure of the same secret “in an abusive manner” 38.

The protection of trade secrets in the context of a society characterized by an elevated digitalization of information as well as by cloud computing services is becoming increasingly important; it is in this context that the European legislator is prompted to attempt the harmonization of the laws of the Member States on the subject. Already in 2011, a study was commissioned to understand which were the most significant elements that had to be taken into account for a harmonized and strengthened protection of secrecy; the findings of this study led to the adoption of EU Directive 2016/943 of 8 June 2016 «On the protection of confidential know–how and confidential business information (trade secrets) against unlawful acquisition, use and disclosure» 39.

The Directive was implemented in Italian legal system by means of Legislative Decree no. 63 of 11 May 2018 40, in the context of which, apart from a change on a linguistic level, in that the wording “secret information” has been abandoned and we now speak of trade secrets or know–how 41, the secrecy requirements necessary to qualify secrets as such have been substantially maintained, but the scope and incisiveness of the protection have been broadened, as culpable conduct is now also punished and special powers have been granted to the judge in proceedings concerning the unlawful acquisition, disclosure or use of secrets, in order to prevent those same secrets from being disclosed or disseminated in this way. In the same vein, moreover, the legislator has also provided for a tightening of the penalties that may be imposed by the judge, establishing, however, that as an alternative to the application of precautionary measures, such as the injunction and withdrawal from the market, the judge may authorize the continuation of the infringing activity, provided that a security is paid for an appropriate amount for the purpose of any compensation for damages suffered by the legitimate holder 42.

All this being said, the reason why proceedings on infringement of secrets are of interest here derives from the evidentiary difficulties that often arise in these judgments, whether provisional or on the merits; it is sufficient to think of the difficulties that a company may encounter when one of its former employees decides to resign and accepts a job offer from a competitor and the company itself fears that the employee has stolen some of its trade secrets in order to make them available to the new employer. In these cases, the practice of Italian courts is to grant—obviously on the basis of justified claims—industrial description orders inaudita altera parte, aimed at allowing the owner of the secrets to obtain proof of their violation; such acquisition is permitted by providing that a computer forensics expert can access all the devices of the former employee and the new employer of the same, acquiring all the data present on the devices in question and possibly also paper data that are scanned and thus become digital data themselves.

Bearing in mind that the judge normally issues an order in which he also identifies all the safeguards that must be adopted in order to avoid the disclosure, in the course of the proceedings, of the secrets for the protection of which the order has been granted.

It is clear that, for the purposes of identifying in the bulk of the documents acquired, those that are relevant, the use is normally envisaged, by the computer forensics expert, in contradistinction with the consultants appointed by the parties and endowed with the same skills, of software that is able to make this selection, on the basis of keywords 43. The use of these tools therefore provides for the prior elaboration, by the judge, of the keywords on the basis of which the search will be carried out, which normally consist, purely by way of example, of the names of the companies, the names of their clients or even the names of the secrets themselves, for example “recipe for paradise cake”; moreover, the keywords in question are very often the subject of discussion during meetings between the computer forensics experts involved.

With a view to the future use of more complex artificial intelligence tools, such as the more advanced Natural Language Processing tools, to a certain extent the identification of relations between inferred facts, facts and evidentiary instances could be more precise, provided that the artificial intelligence tools are adequately trained.

It is believed that greater accuracy can be acquired through the development of computational language models. Linguistic modelling is a form of unsupervised learning underlying the processing of unstructured data. Text Mining and Text Preprocessing techniques appear to be particularly relevant in this respect. Text Mining, unlike Data Mining, which includes all techniques for analyzing and interpreting quantitative data generally derived from observations in science or economics, incorporates all statistical techniques and algorithms for the classification, automatic extraction of information and grouping of text documents; it is based on the retrieval of information from text documents, which are, however, in a highly unstructured and confused format, so that the information is lost in “a sea of words”. Text Preprocessing consists of a series of operations aimed at cleaning the text of all those linguistic forms that may pollute the subsequent retrieval of information.

The purpose of Text Preprocessing is to convert the text in such a way as to put in the best possible conditions both the algorithms that, through the tools of Natural Language Processing, will have the task of describing the words through a multi–vector representation, and the algorithms in charge of extracting the information to characterize the texts under examination 44. After Text Processing is the vector extraction phase, and this is done specifically by means of the Word Embedding technique 45, precisely that technique that allows the representation of sentences and words through the use of vectors 46. Based on methodologies from machine learning, it is in fact possible to exploit models that automatically extract from documents the information needed to train neural networks. By means of Word Embedding, it is possible to decode the semantic and syntactic information of words, and this technique makes it possible to identify the relationships existing between words contained in different texts. Certainly this methodology is difficult to understand for anyone who does not have, like the writer, a real expertise in computer science, however, in light of the results obtained to date by computer science, it is believed that these Word Embedding methodologies can be used for a number of purposes and, therefore, also in order to improve operations of classification of factual circumstances and subsumption of the same in the cases invoked in order to identify the limits of the evidentiary instances.

7. Conclusions: a recent Italian initiative for new technologies in civil justice (Next Generation UPP)

In conclusion, the implementation of artificial intelligence and new technologies can be a concrete help to improve the efficiency of justice as well as a tool that allows for greater legal certainty, which—as anticipated—contributes to a better distribution of available resources in order to avoid their depletion.

In this respect, attention can be focused on a very important Italian initiative—precisely aimed at the implementation of artificial intelligence and new technologies in the civil justice system—called Next Generation UPP.

Next Generation UPP—UPP stands for “Ufficio per il processo” and we are going to translated it as “Trial Offices”—is a project of the University of Turin, Departments of Law, Computer Science and Management, in partnership with eleven universities in North-West Italy. The project is part of the «Unified Project for the Dissemination of the Trial Office 47 and the Implementation of Innovative Operational Models in Judicial Offices for the Disposal of the Backlog» promoted by the Ministry of Justice under the NOP Governance and Institutional Capacity 2014–2020 and implemented in synergy with the interventions included in the National Recovery and Resilience Plan (PNRR) in support of civil justice reform that had been enacted quite recently and will be in force at the end of this month, namely on February 28, 2023 48.

The project aims to improve the performance of justice in Northwest Italy through the strengthening of trial offices, experimentation with new collaborative schemes between universities and judicial offices, and technological innovation. These objectives had been considered worthy of funding and consistent with Action 1.4.1 of the NOP Governance and Institutional Capacity (2014–2020) and by the Directorate General for the Coordination of Cohesion Policies of the Ministry of Justice.

Next Generation UPP involves the judicial offices of courts of appeal of Brescia, Genoa, Milan and Turin, the courts of the relevant districts and the juvenile courts that actually recently the civil justice reformed severely.

With the approval of the PNRR, Italy made a commitment to the European Commission to achieve specific targets, with reference to the reduction of the length of proceedings and the elimination of the ultra–long civil and criminal backlog, in accordance with the reasonable duration of trials. To help maintain the commitments made and improve the performance of justice in northwest Italy, the project intends to adopt a multidisciplinary approach at each stage by harnessing legal, business and IT expertise.

The purpose is to achieve a structural reform that, by means of artificial intelligence, economic–managerial control, change management and digitization tools, will ensure that an organization can function efficiently, rationally and in a coordinated manner by correcting cognitive biases and avoiding waste of time and resources.

Next Generation UPP project is consistent with what the Council of Europe’s European Commission for the Efficiency of Justice (CEPEJ) highlighted in the European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and their environment, aims to enhance the digitization process of judicial activities as a basis for the application of Legal Analytics (LA) tools and to create a technical environment in which different disciplines, including data science, artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), can converge.

The project coordinators agree that AI is merely meant to support the human operator, not replace it. In fact, AI is intended to expand the tools of knowledge and analysis, in fact and in law, available to the constitutionally autonomous magistrate, so that he or she becomes authentically aware of his or her choices.

In this context a prominent role has been recognized to the Trial Offices and the staff employed therein, present and future. Over the next five years, the Trial Offices structure will undergo a temporary, but substantial change due to the PNRR–funded intake of 16,500 new employees destined to be exhausted at the end of 2026. The challenge is therefore twofold: to make the most of this once–in–a–lifetime opportunity by managing term staff in the best possible way, and to properly set up Trial Offices so that they continue to perform their functions efficiently once the extraordinary workforce is exhausted.

Thus, the first line of action concerns the «definition of operational forms for the establishment and implementation of the Trial Offices».

After an initial phase of acknowledgment regarding the functioning of the judicial offices at which the Trial Offices are active and those that still lack them, the project envisages the «definition of the catalog of activities and procedures for the activation and strengthening of the Trial Offices» 49.

In concrete terms, this action will see the implementation of some of the following operational modules: supporting the writing of the motivation of civil and criminal measures; creation of the conceptual sentence file–model; document builder systems; semi–automatic anonymization of judicial decisions; semantic analysis tools for judgments; study and prototyping of tools for automatic extraction of information from documents submitted by the parties; automatic classification of documents in trial folders and their correlation; use of AI techniques for precedent management; enhancement of the existing digital; case law databases.

The second line of intervention is aimed at «identifying models for the management of incoming flows and backlogs at Court Offices». The project makes use of the legal, IT and business skills of the recruited resources by implementing the following activities: economic–managerial domain control over reconnaissance activities; identification of offices for testbed and support for formalization of processes modeling of flows for the testbed; assessment of testbed activities.

The third line of intervention involves the «Activation and testing of models and plans related to the previous actions» through the establishment of a Task Force to deal with the activation and testing of the models and plans developed during the project and to assess their capacity building and extension at the national level as well.

The fourth line of intervention acts on the role of university institutions and their relationship with the world of civil justice. The desire is to define new collaborative patterns based on sharing of objectives, collaboration, experimentation capacity, identification of potential, and development of organizational capacity, in order to improve justice services for citizens and territories.

Next generation UPP identifies activities to be carried out in the context of the single— and three—year master’s degree program in the legal disciplines and activities to be carried out in the context of postgraduate offerings, such as: changing the syllabi and examination methods of the fundamental courses for the training of the future practitioner of jurisdiction, with greater emphasis on the acquisition of skills necessary for the new market of jurisdiction, primarily the ability to draw up concept maps and approach practical cases; writing workshops, legal informatics workshops, legal clinics for deserving students in the single–cycle master’s degree program; training of graduate student scholars and/or fellows to be sent to judicial offices in the Northwest area as part of Next Generation UPP; training of trial clerks recruited through the extraordinary examination phase under the NRP–funded recruitment plan; higher interdisciplinary training courses or executive master’s programs.

Clearly the objectives of the project are different and on different levels.

With respect to the situation of the Trial Offices the aim is to safeguard and improve the UPP facility projected beyond 2026, when the activity of the extraordinary workforce envisaged by d.l. 80/2021 and funded by the PNRR will end as well as to achieve—during the implementation period—a more efficient management of incoming litigation and a reduction in the civil backlog by the judicial offices involved.

With respect to the training context, the main objective is to be able to train law graduates equipped with the skills necessary to be placed in the Trial Offices by minimizing the need for on–the–job training by judicial offices and initiate with judicial offices (and bar associations) new forms of postgraduate collaboration suitable for meeting the needs of the justice market.

The final goal is both to have judicial offices in the relevant Macro Area who could have acquired a more efficient method of managing judicial affairs and utilizing the Trial Offices in order to contribute to the reduction of the backlog of cases and reduce the average duration of proceedings and to have law graduates in the Macro Area who could have acquired soft skills, IT and management skills for inclusion in the Trial Offices.

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