The authors were recently granted a project in which they collaborate with judges from several jurisdictions in Belgium. The concrete goal of the project is to assist the Chambers for Distressed Enterprises in evaluating the economic prospects of selected enterprises. The implicit overarching goal is to introduce artificial intelligence in the Belgian legal landscape, an ambitious undertaking given that Belgium lacks behind most other European countries when it comes to digitisation in court. The project, as well as the background of the authors in artificial intelligence and law, puts them in a unique position to evaluate the failures, challenges and opportunities of the application of artificial intelligence in Belgian court. The obstacles that need to be tackled to prepare Belgian court for the era of an ubiquitous use of artificial intelligence, are illustrated by describing the seemingly endless eforts that are being made to introduce an online database that is to contain all judicial decisions. The observation that Belgian court operates, to some extent, in an archaic way, provides at once many opportunities for eficiency improvements by relying on artificial intelligence. This is illustrated by describing two specific potential applications of the use of artificial intelligence in Belgian court. The ideas and methods underlying these applications may as well find implementation in other judicial matters and in foreign jurisdictions.
parts of the world
Digitisation is an important building block for the development of artificial intelligence tools as
it provides the necessary infrastructure and data. Across the globe, various initiatives have been
taken to stimulate digitisation of the legal domain, some more ambitious than others. In China,
the Supreme People’s Court, endorsed judicial transparency and created the ’China Judgements
Online’ database in 2014 [
In China, most technological expertise related to AI resides in the private sector, rather than
in courts, according to Stern et al. (2020). Therefore, Chinese courts have been purchasing
support from technological companies which enabled the rapid development of AI tools [
Recently, we were granted a project1 that has as purpose to support judges in the Chambers for Distressed Enterprises in the detection and evaluation of distressed firms. The judicial role of these Chambers is to safeguard creditor’s rights. While we are currently still in the stage of developing some preliminary machine learning methods, it was clear that basic techniques and accompanying terminology are beyond the judges’ background knowledge. Therefore, the specific purpose of the project described above, should merely be considered as a vehicle to introduce judges to the field of AI and to arouse enthusiasm in relying on ditto methods. Our project, and the lively discussions we had with judges in this context, provide an ideal opportunity to describe some peculiarities in the digital transformation of court in a small EU country that does not perform as one would expect from an otherwise highly developed country2. We contribute to literature by (1) providing a non-exhaustive description of the state of artificial intelligence in Belgian court, positioned in a global context, and (2) discuss two potential applications of AI in court that can prove useful in the future, once the remaining challenges are met.
In Section 2 we describe the position of Belgium with respect to other EU countries in terms of using digital technology in court. Furthermore, we illustrate the struggles that are encountered in digitising Belgian court by describing the seemingly endless eforts that are being made to introduce an online database that is to contain all judicial decisions. Section 3 describes two specific potential applications of the use of artificial intelligence in Belgian court. The ideas and methods underlying these applications may as well find implementation in foreign jurisdictions.
1Project web page: https://researchportal.be/en/project/evaluation-distressed-enterprises-using-explainable-m achine-learning-construction
2E.g., in terms of the Human Development Index, Belgium ranks 17th among 186 listed countries, cf. https: //worldpopulationreview.com/country-rankings/hdi-by-country.
The Next Generation EU fund is a European initiative to provide financial support to all member
states to recover from the adverse efects of the COVID-19 pandemic [
Compared to the judicial systems in other EU Member States, the Belgian system is among the
worse performing when it comes to digitisation. One noteworthy indicator for this statement
is the EU Justice Scoreboard, which is an annual comparative information tool that was first
launched in March 2013 by the European Commission [
The first serious attempt to introduce digital methods in the Belgian judicial system came with
the law of 20 October 20003 for introducing the use of telecommunications and electronic
signatures in judicial and extrajudicial proceedings. This law introduced the possibility for
sending certain notifications via e-mail in the aforementioned kind of proceedings. Later, the
law of 10 August 20054 had as general purpose to anticipate a new judicial information structure
by the introduction of the Phenix data system [
In 2008 another major project was launched, called the Cheops project [
Another significant project, called JustX, was launched in 2012 [
The latest development in the digitisation process is the introduction of an online database
that contains all judicial decisions, expected to be implemented at the beginning of 2023 [
5In the meantime replaced by JUPORTAL, cf. https://juportal.be/home/welkom
In the previous section, we discussed the failures and challenges related to the digitisation of Belgian court. On a more positive note, in this section we describe the opportunities of artificial intelligence through two potential use cases: (1) the detection and evaluation of distressed firms in enterprise court and (2) sentencing speed penalties in police court. We have selected these cases as the research project, described in Section 1.2, allowed us to collaborate with judges, in particular judges from enterprise court. The insights in the working principles applied by the magistrates has led us to formulate these use cases, which we believe are illustrative for the tremendous positive impact that AI can have on the operation of the courts. Although the suggested applications are described with the Belgian context in mind, the use undoubtedly extends to courts in other countries.
The goal of the project is to develop a tool that assists judges in insolvency proceedings. In Belgium, insolvency proceedings are described in Book XX of the Economic Law Code (ELC). The main idea of Book XX is that prevention is better than cure (art. XX.21-29 ELC). To accomplish this rationale, the Enterprise Court contains Chambers for Distressed Enterprises (art. XX.25 ELC), composed of one professional judge and several non-professional judges, with as task the evaluation of an enterprise’s economic prospects upon notification of so-called “flashing lights” that indicate that the enterprise is encountering financial dificulties (as an example, one flashing light is that social security contributions have not been paid for two quarters). The evaluation will result in the dismissal of the case if the flashing lights are not alarming. If, on the other hand, the flashing lights are disturbing, the representatives of the enterprise can be summoned to court, with as purpose to start a dialogue on how to remedy the situation (in the spirit of “prevention is better than cure”). The conclusion of the evaluation could, however, also be that the enterprise is in state of bankruptcy, in which case the crown prosecutor will be notified (art. XX.29 ELC), who might subsequently sue in bankruptcy.
We had the opportunity to attend internal meetings of the Chambers for Distressed Enterprises, which allowed us to gain insight in the criteria used to evaluate the economic prospects of an enterprise. We believe that the employed methods are not eficient and there is much room for improvement. For instance, due to time constraints, the business data taken into account is limited. Furthermore, when handling the data, no computer input-output model was used and the process was guided by experience and heuristics. For example, in one of the Chambers, all companies with alarming flashing lights are sorted according to an arbitrarily selected flashing light each month. Depending on the available resources of the court that month, a relatively small number of firms is then selected for further evaluation and analysis.
A concrete goal of the aforementioned project is to build a tool capable of processing numeric
and textual data from (mainly) annual reports that allows to rank companies according to
bankruptcy risk. As a first step in the development of the system, a benchmark for the textual
data scenario and several baseline models have been released. We have treated the task as
an (imbalanced) binary classification problem and used bag-of-words techniques and a neural
sequence encoder (the Longformer model [
The Belgian trafic landscape is divided into diferent zones, where for each zone a specific speed limit applies. For example, in built-up areas, which are typically densely populated, a speed limit of 50 kilometres per hour applies, unless there is a sign that indicates otherwise. Art. 29, §3 of the Road Trafic Law stipulates that speeding is sentenced with a fine between 10 EUR and 500 EUR. However, due to a peculiarity of the law, the pronounced fine needs to be multiplied by 8 to give the actual amount that the ofender will be required to pay. Thus, the actual fine may vary from 80 EUR to 4 000 EUR. The law stipulates that “the judge takes the excess speed into account in determining the fine”, without specifying any relationship between excess speed and ifne. The judge has, therefore, full discretion in determining an appropriate fine. Furthermore, art. 29, §4 of the aforementioned law stipulates that the fine may be reduced by the judge to an amount that is less than 10 EUR, without being smaller than 1 EUR, if the details of the case urge a milder fine. The law does not describe any specific circumstance that imposes the judge to grant such a reduction and to what extent such favourable peculiarities have an influence on the pronounced fine. The main consequence of the wide judicial discretion in matters of speeding is evident: case law across jurisdictions is non-uniform. Lawyers working in the domain of trafic law often know which judges are particularly mild or exceptionally strict. For litigants, however, it might be puzzling why they were sentenced a fine that is higher than the ifne to be paid by a friend. High workload, combined with time constraints, are also factors contributing to confusing fines.
In recent work we describe how artificial intelligence might be used to reduce legal uncertainty in matters involving ex aequo et bono compensations, which refer to fair estimates of several kinds of damages that cannot be determined otherwise, in particular damages that relate somehow to prevailing moral or societal values [18]. We suggest to represent each case through a feature vector x and the assigned ex aequo et bono compensation as a random variable J(x). Legal uncertainty can now be quantified through ( ) = ( [̂ (())̄] ) − with [̂ ()] being the sample standard deviation and (̄) the sample mean of J(x) and is a parameter that must be chosen. Intuitively speaking, we proposed to use previous similar cases as training examples to estimate an appropriate compensation amount for the case at hand, which reduces legal uncertainty essentially to zero. In order to avoid that compensations become out of sync with contemporary moral or societal values, we envisage an expert committee of judges that has the authority to decide that the considered class of cases should, from that moment on, be handled by judges without support of the AI tool. From a machine learning point of view, this simply means that all training examples are discarded, and that a new training set is to be build up through judicial case law. It is only when case law has stabilised to a new equilibrium that the machine is again given the role of generating appropriate ex aequo et bono compensations.
This method might equally well be applied in generating speeding fines based on case law. The ofender could still be given the possibility to bring forth arguments that relate to circumstances that are to his advantage and that justify a fine that is milder than the one suggested by the artificial intelligence tool. If the ofender accepts the machine generated fine, the judge might simply approve it. Especially this latter case might entail tremendous benefits for the litigant. First, the case might be handled very soon after the speeding ofence. Currently, it is no exception that even simple speeding cases are only handled one year after the ofence. In this respect it is unsurprising that the law of 6 March 20186 concerning the improvement of trafic safety extended the period of limitation from one to two years. Secondly, legal uncertainty would essentially be reduced to zero. The artificial intelligence tool might be made publicly available, such that litigants are able to verify the machine generated fine, given the details of the case, even before they are sued. Thirdly, a machine generated fine that is not contested, implies that the assistance of a lawyer is not needed, thereby avoiding additional costs that might otherwise be significant. Indeed, nowadays the assistance of a lawyer is frequently advised, even for undisputed ofences, since an experienced lawyer knows which arguments are best raised in order to persuade the case-handling judge to rule mildly. Arguments that are dependent on the deciding judge are, obviously, useless in case a machine generates the fine.
Undoubtedly, the introduction of artificial intelligence in court would be beneficial to the judicial system. Eficiency improvements, reduction of legal uncertainty and lower costs for litigants are just some examples of the advantages that AI can bring. However, digitisation is an important building block that needs to be put in place before AI systems can be developed, as it provides the required infrastructure and data. All around the world, initiatives are taken to achieve this goal with China aspiring to become the global leader in this regard. When it comes to Belgium, a long history of digitisation eforts have proven to be a fruitless endeavour with diferent projects ending prematurely without achieving the foreseen goals. Despite the failures from the past, ongoing initiatives are trying to tackle the digitisation challenge, for instance, through the introduction of an online database containing all judicial decisions. As a way forward, we have discussed two potential applications of artificial intelligence in court. First, AI can be used in enterprise court for the detection of distressed enterprises. Such a system can lower the workload for the magistrates involved and ensure a more uniform evaluation of the enterprises. Second, legal uncertainty can be reduced in cases involving ex aequo et bono compensation such as speeding penalties handled by police court.
This work was supported by the Research Foundation - Flanders (Grant number G006421N). [17] H. Arno, K. Mulier, J. Baeck, T. Demeester, Next-year bankruptcy prediction from textual data: benchmark and baselines, Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing FinNLP@IJCAI2022 (2022) 36–42. [18] W. De Mulder, P. Valcke, J. Baeck, A collaboration between judge and machine to reduce legal uncertainty in disputes concerning ex aequo et bono compensations, Artificial Intelligence and Law (2022) 1–9.