In our research group working on normative systems, we develop (Normative) Agent Based Models for evaluating policies, and as a basis for building distributed (normative) control components. If and how interactions between actors (represented by agents) take place are heavily impacted by the (dis)trust between those actors. In this paper, we discuss a model of the representation of the three components of the agent’s trustworthiness: competence, benevolence, and integrity. The model presented in this paper is being illustrated by a small simulation experiment.

The paper addresses the problem of making legal decisions about regulations such that they are compliant with legal principles. While decision-making using legal rules is well-developed in the literature, few researchers address reasoning with principles. A key difference between applying rules versus principles is that rules have a binary character, they are applied or not, while principles can be applied to a certain extent and in the case of conflicting principles, they require balancing instead of defeat. This paper provides a novel approach to balancing principles and deciding amongst regulations by proportional optimisation of values associated with regulations and principles. The approach can be generalised to other areas of decision-making.

This paper presents a framework for evaluating approaches to formalizing legal sources. The framework integrates adoption-related criteria, extending the scope of assessment beyond purely technical dimensions. We build upon three contemporary comparison frameworks for normative specification languages, combining the Technology-Organization-Environment framework with grounded theory and adding criteria from other IT fields. As an illustration, we applied the resulting framework to RegelSpraak (RuleSpeak) from the Dutch Tax Administration.

In recent years, several normative systems have been presented in the literature. Relying on formal methods, these systems support the encoding of legal rules into machine-readable formats, enabling, e.g. to check whether a certain workflow satisfies or agents abide by these rules. However, not all rules can be easily expressed (see for instance the unclear boundaries between tax planning and tax avoidance). The paper introduces a framework for norm identification and norm induction that automates the formalization of norms about non-compliant behavior by exploring the behavioral space via simulation, and integrating inputs from humans via active learning. The proposed problem formulation sets also a bridge between AI & law and more general branches of AI concerned by the adaptation of artificial agents to human directives.

The purpose limitation principle is a GDPR cornerstone that aims to minimize data processing risks by limiting instances of personal data access and usage. We model purpose as an action or sequences of actions and formalize action relationships to derive purpose-based permissions. Based on these permissions, we introduce a novel purpose-based access control model with a purpose matching algorithm illustrated with a healthcare research use case.

With the uptake of digital services in public and private sectors, the formalization of laws is attracting increasing attention. Yet, non-compliant fraudulent behaviours (money laundering, tax evasion, etc.)—practical realizations of violations of law—remain very difficult to formalize, as one does not know the exact formal rules that define such violations. The present work introduces a methodological framework aiming to discover non-compliance through compressed representations of behaviour, considering a fraudulent agent that explores via simulation the space of possible non-compliant behaviours in a given social domain. The framework is founded on a combination of utility maximization and active learning. We illustrate its application on a simple social domain. The results are promising, and seemingly reduce the gap on fundamental questions in AI and Law, although this comes at the cost of developing complex models of the simulation environment, and sophisticated reasoning models of the fraudulent agent.

The paper presents a formal model and an experimental verification of the system controlling the International Humanitarian Law compliance for the autonomous military device.

Legal texts are often difficult to interpret, and people who interpret them need to make choices about the interpretation. To improve transparency, the interpretation of a legal text can be made explicit by formalising it. However, creating formalised representations of legal texts manually is quite labour-intensive. In this paper, we describe a method to extract structured representations in the Flint language (van Doesburg and van Engers, 2019) from natural language. Automated extraction of knowledge representation not only makes the interpretation and modelling efforts more efficient, it also contributes to reducing inter-coder dependencies. The Flint language offers a formal model that enables the interpretation of legal text by describing the norms in these texts as acts, facts and duties. To extract the components of a Flint representation, we use a rule-based method and a transformer-based method. In the transformer-based method we fine-tune the last layer with annotated legal texts. The results show that the transformed-based method (80% accuracy) outperforms the rule-based method (42% accuracy) on the Dutch Aliens Act. This indicates that the transformer-based method is a promising approach of automatically extracting Flint frames.

Software systems that share potentially sensitive data are subjected to laws, regulations, policies and/or contracts. The monitoring, control and enforcement processes applied to these systems are currently to a large extent manual, which we rather automate by embedding the processes as dedicated and adaptable software services in order to improve efficiency and effectiveness. This approach requires such regulatory services to be closely aligned with a formal description of the relevant norms.

The combination of smart contracts with blockchain technology enables the authentication of the contract and limits the risks of non-compliance. In principle, smart contracts can be processed more efficiently compared to traditional paper-based contracts. However, current smart contracts have very limited capabilities with respect to normative representations, making them too distant from actual contracts. In order to reduce this gap, the paper presents an architectural analysis to see the role of computational artifacts in terms of various ex-ante and ex-post enforcement mechanisms. The proposed framework is assessed using scenarios concerning data-sharing operations bound by legal requirements from the General Data Protection Regulation (GDPR) and data-sharing agreements.

This paper aims to set up a conceptual framework for studying the second-order guidance problem—that is, designing coordination mechanisms for autonomous actors by means of adequate monitoring and enforcement measures—in a way which is sensible for designers and users of data-sharing infrastructures such as digital market-places. The paper outlines a minimal, but reusable and extensible computational model to test the sustainability of diverse norm implementations, evaluating it against relevant higher-level models presented in the literature.

In the field of AI and Law, there is a debate whether normative relations can be expressed using only deontic concepts versus the opinion that a potestative perspective on norms cannot be reduced to deontic expressions. Makinson, Jones and Sergot are proponents of the latter view. In this paper, we will expand on their examples of priests marrying couples of mixed religions, and couples married by former priests, in order to better understand the notion of power.

In the last years governments started to adapt new types of Artificial Intelligence (AI), particularly sub-symbolic data-driven AI, after having used more traditional types of AI since the mid-eighties of past century. The models generated by such sub-symbolic AI technologies, such as machine learning and deep learning are generally hard to understand, even by AI-experts. In many use contexts it is essential though that organisations that apply AI in their decision-making processes produce decisions that are explainable, transparent and comply with the rules set by law. This study is focused on the current developments of AI within governments and it aims to provide citizens with a good motivation of (partly) automated decisions. For this study a framework to assess the quality of explanations of legal decisions by public administrations was developed. It was found that communication with the citizen can be improved by providing a more interactive way to explain those decisions. Citizens could be offered more insights into the specific components of the decision made, the calculations applied and sources of law that contain the rules underlying the decision-making process.

In order to effectively implement guidance structures in a computational social system, directives which are specified in general terms of duties and rights need to be transformed in terms of powers and liabilities attributed to social parties. The present paper is a work in progress report on an axiomatization of power structures in a logic programming setting, covering the intentional level in specifying actions, the connection between productive characterization of actions and causation, the default nature of action specifications, failures and omissions, the relations of causation and power, and the concept of interfering actions.

Norms are driving forces in social systems and governing many aspects of individual and group decision-making. Various scholars use agent based models for modeling such social systems, however, the normative component of these models is often neglected or relies on oversimplified probabilistic models. Within the multi-agent research community, the study of norm emergence, compliance and adoption has resulted in new architectures and standards for normative agents. We propose the N-BDI* architecture by extending the Belief-Desire and Intention (BDI) agents’ control loop, for constructing normative agents to model social systems; the aim of our research to create a better basis for studying the effects of norms on a society of agents. In this paper, we focus on how norms can be used to create so-called Secure Trustworthy Digital Marketplaces (STDMPs). We also present a case study showing the usage of our architecture for monitoring the STDMP-members’ behavior. As a concrete resu lt, a preliminary implementation of the STDMP framework has been implemented in multi-agent systems based on Jadex.

This paper introduces a methodology for the acquisition of the computational model of a service provider group and its transformation into agent-based model. The methodology is as follows. First, we analyze the case at the signal layer, i.e. the message exchange between actors, and model them with the components of “belief, desire and intention (BDI)” agent architecture. In the next step, we identify the implicit actions, intentions, and conditions which are necessary for the story to occur. These steps correspond to descriptions of agent-roles observed in the case study. As a concrete result, a preliminary implementation of the framework has been developed with Groovy.

Methods for the formal interpretation of normative sources in natural language, e.g. Statute Law and regulations form a neglected part of the field of AI and Law. In our view a frame-based approach is best suited for making formal specifications for normative systems that can be traced back to normative sources. The adequacy of the Flint-language to perform this task, is being compared with that of two existing frame-based solutions.

In this paper, we describe our work towards a method for a formal analysis of law. The Dutch Immigration and Naturalization Service (IND) is responsible for the implementation and execution of complex and ever changing regulations. Given the amount of cases to handle, the use of IT systems is a necessity. From 2007 the IND, being aware of their dependence on trustworthy methods to assure the correct implementation of law into their operations and services, have been working on developing an approach that enables them to 'translate' the legal rules expressed in natural language to specifications in computer executable form. In this paper, we will explain this approach and illustrate it with some concrete examples. The work is part of a larger innovation programme initiative that we collaboratively conduct within a virtual collaboration, called the 'Blue Chamber'.

To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.

The paper introduces elements of a methodology for the acquisition of descriptions of social scenarios (e.g. cases) and for their synthesis to agent-based models. It proceeds along three steps. First, the case is analyzed at signal layer, i.e. the messages exchanged between actors. Second, the signal layer is enriched with implicit actions, intentions, and conditions necessary for the story to occur. This elicitation is based on elements provided with the story, common-sense, expert knowledge and direct interaction with the narrator. Third, the resulting scenario representation is synthesized as agent programs. These scripts correspond to descriptions of agent-roles observed in that social setting.

Rather than as abstract entities, jural relations are analyzed in terms of the bindings they create on the individual behaviour of concurrent social agents. Investigating a simple sale transaction modeled with Petri Nets, we argue that the concepts on the two Hohfeldian squares rely on the implicit reference to a “transcendental” collective entity, to which the two parties believe or are believed to belong. From this perspective, we observe that both liabilities and duties are associated to obligations, respectively of an epistemic or practical nature. The fundamental legal concepts defined by Hohfeld are revisited accordingly, leading to the construction of two Hohfeldian prisms.

This article presents a conceptual framework intended to describe and to abstract cases or scenarios of compliance and non-compliance. These scenarios are collected in order to be animated in an agent-based platform for purposes of design and validation of both new regulations and new implementations, or to be used as reference base for a diagnosis tool. In our approach, legal narratives become a source of agent-roles descriptions, i.e. abstractions of individual characters/agents from singular stories, feeding the target applicative framework.

This paper proposes a knowledge elicitation method based on serious gaming for theory construction about the effects of the law on the behaviours of agents. These games provide input to simulations of business process and product design alternatives. For knowledge representation, we have combined agent role descriptions with a generic task framework. An important thesis of this paper is that, in the interest of quick and simple domain analysis, agent roles, not intelligent agents, should be the focal object of simulation of complex social organizations. At least if getting a grip on social complexity is the purpose of modeling.

This paper presents a monitoring and diagnosis component of a knowledge cquisition, design, and simulation framework for implementation of compliance in public administration. A major purpose of the framework is to give a methodological justification for the exploration of compliance control policies. The knowledge acquisition approach depends on the storylike character of relevant case law and expert knowledge, and the compliance controls design space is derived from these stories.

In public administration, attempts to specify a unified interpretation of law seem to end in a specification with little operational meaning at all. The same unit of discourse in the sources of law usually plays many different knowledge roles, and ends up with – sometimes subtly – different operational meanings in each.

Traditionally researchers in the AI&Law community conduct research after legal reasoning, legal knowledge representation and the application of AI-based techniques for supporting legal practitioners or their clients. The focus in AI&Law research, after focusing on legal knowledge based systems has shifted to legal argumentation, which as a topic is very appealing both from a knowledge representation perspective (especially if you have a background in logics) as from a knowledge engineering perspective. However while many researchers working in the field of AI&Law focus on legal argumentation only a few actually build argumentation support systems. Even fewer researchers actually conduct empirical research aimed at supporting lawyers and judges in practical situations. This is a pity since empirical research is a requisite for building systems that are to support lawyers and judges in their daily practice. Furthermore interesting developments are going on in different legal institutions, developments that bring the application of scientific results much closer. The current situation is that only a very small number of argumentation support systems exist, e.g. Auraucaria by Reed and Rowe [1] and Argumed by Verheij [2] and even these systems are not useable in practice. For AI & Law researchers it may be disappointing to experience that the systems that are actually used and considered to be useful are much less advanced compared to the dream ware in the researchers minds. The progress made in legal content management solutions is huge. Key to the success in this field is cooperation in the field of standardization, e.g. in the CEN/Metalex working group focusing on standards for legal sources. In the SEAL project [3] that was sponsored under the European eParticipation programme three different editors for legislation drafting were tested and a first attempt to come up with an open, integrated infrastructure for supporting the legislative chain has been made. Similar approaches have been developed in the judicial domain. Recent studies such as [4], [5] and [6] show that while the use of paper based dossiers is still the common practice within juridical environments some countries have made progress into changing to electronic dossiers and supporting the legal processes using case management and work flow management solutions. Despite the clear advantages that working with completely electronic dossiers has, as is demonstrated e.g. in the Austrian Ministry for Justice [5], many organizations haven't yet turned that into their daily practice yet. Changing dossiers containing paper documents to electronic dossiers containing electronic documents seems at first sight an easy job. But when the process around the handling of paper dossiers is studied carefully, and the types of documents involved, the transformation is not as easy as it seems. Also the traditional way of dealing a paper dossier is completely different from dealing with an electronic dossier. This makes that the acceptance of an electronic dossier by lawyers and judges is sometimes hard to get. In order to meet the requirements of the lawyers and judges, one important requirement is that an electronic dossier shouldn't involve more work than a paper-based dossier. This is one of the main requirements but not the only one. In this paper the development and implementation of electronic dossiers at the Dutch Council of State is described.

This paper describes extensions to our native XML legislative editor MetaVex, dealing with specific requests of legislative drafters: The automatic generation of amending documents based on the editing of an existing law or proposal, and the automatic consolidation of (proposed) legislation based on the original and its amending documents. Moreover, we try to automatically detect (potential) clashes of amendments and amendments of amendments.

More and more people on the working floor are expected to have knowledge of the sources of law that are applicable to their field. While coping with vast volumes of regulations is a challenge, dealing with changes in legislation is even more challenging. Organizational change also affects the amount and complexity of legal rules people in organizations have to deal with. Moreover, not only rules are affected; changes in the organization's environment often create the need to redesign business processes and IT infrastructure, reallocate roles and responsibilities and reorder tasks. This paper explains our approach on dealing with these issues.

This paper argues that the best chance of utilising AI & Law models of legal argument in practice in the near future may be in desiging legal argumentation management systems. Such systems do not have a knowledge base and inference engine but allow the user to structure a legal case dossier in terms of the argumentation structure of a case, so that better overview over the dossier is obtained and documents can be better retrieved, compared and drafted. To obtain insight in how such systems should be designed, a case study is presented in which a dossier of a solicitor representing a client in a Dutch civil dispute is analysed according to its argumentation structure. The resulting structures are visualised using the Araucaria software tool. Special attention is paid to the creation of the argumentation structure over time in the course of the dispute, and to the argumentative tactics and strategies employed by the solicitor.

The Dutch Tax and Customs Administration (DTCA) is one of many organizations that deal with a multitude of electronic legal data, from various sources and in different formats. In this paper, we describe the results of a study aimed at better access to these sources by having a supplier and format independent knowledge store that describes the sources and their interrelations in a semantic network. Furthermore we developed parsers to automatically detect the identity of sources and typed references within the sources to other legal documents. These parsers can be used to fill and update the semantic network as new documents are added.

Public administrations of all over the world invest an enormous amount of resources in e-government. How the success of e-government can be measured is often not clear. E-government involves many aspects of public administration ranging from introducing new technology to business process (re-)engineering. The measurement of the effectiveness of e-government is a complicated endeavor.In this paper current practices of e-government measurement are evaluated. A number of limitations of current measurement instruments are identified. Measurement focuses predominantly on the front (primarily counting the number of services offered) and not on the back-office processes. Interpretation of measures is difficult as all existing measurement instruments lack a framework depicting the relationships between the indicators and the use of resources. The different measures may fit the aim of the owners of the e-governmental services, however, due to conflicting aims and priorities little agreement exists on a uniform set of measures, needed for comparison of e-government development. Traditional methods of measuring e-government impact and resource usage fall short of the richness of data required for the effective evaluation of e-government strategies.

In the last decades the interst in the problem of comparing and harmonizing legislation has been steadily increasing. One reason is the increasing legal convergence between governments in the European Union, and the increasing traffic of people over borders of jurisdictions. Another reason is the increasing globalization of companies; Products and services are offered in many jurisdictions at the same time, and the product or service has to meet the provisions of all jurisdictions in which it is offered. In the E-POWER project relevant tax legislation and business processes are modeled in UML to improve the speed and efficiency with which the Dutch Tax and Customs Administration can implement decision support systems for internal use and for its clients. These conceptual models have also proven their usefulness for efficient and effective analysis of draft legislation. We are currently researching whether conceptual modeling can also be used to compare 'similar' legislation from different jurisdictions to improve the capacity of the Dutch Tax and Customs Administration to react to future consequences of increased movement of people, products, and money between EU member states and increased harmonization between tax authorities in Europe. In addition, addressing the problem of comparing models is also expected to improve our methodology for modeling legislation. This paper discusses problems and requirements of comparing legislation as we understand them now, and attempts to relate them to relevant research.

Problems with legal quality will not only increase effort and costs of the law enforcement organisations, but also undermines the regulating power of the legislator. Unintended use or even abuse of the law may be the result. Governments therefore should improve their legal quality. The complexity of legislation however makes this task a hard one. The Dutch Tax and Customs Administration (DTCA in Dutch: Belastingdienst) has developed a method and supporting tools that support a systematic translation of (new) legislation into the DTCA's processes. This POWER-method and tools help to improve the quality of (new) legislation and codify the knowledge used in the translation processes in which legislation and regulations are transformed into procedures, computer programs and other designs. Thereby the time-to-market of the implementation of legislation will be reduced. In this article we explain some knowledge representation techniques that we use to improve legal quality. We will also show its application and give real-life examples of anomalies detected. In contrast to other knowledge modelling approaches the POWER-approach is focused on modelling legal sources rather than expert knowledge. Expert knowledge however is still needed to find the correct interpretations but also for efficiency reasons. Starting with representing the (legal) experts' knowledge (using scenarios) helps us to find the adequate scope (the legal sources to be analysed). Confronting the expert with differences between the model build out of the experts' knowledge and the ones we make out of the other knowledge sources (specifically the law) causes the legal experts to see things in a different light and has often led to changes in the law.

The Dutch Tax and Customs Administration (DTCA in Dutch: Belastingdienst) conducts a research program POWER in which methods and tools are developed that support a systematic translation of (new) legislation into the DTCA's processes. The methods and tools developed help to improve the quality of (new) legislation and codify the knowledge used in the translation processes in which legislation and regulations are transformed into procedures, computer programs and other designs. Thereby the time-to-market of the implementation of legislation will be reduced. In this article we focus on the method we developed for modeling legislation. We will elaborate upon the principles behind the method and explain the use of Catalysis and UML/OCL in the modeling process. The coupling of models of legislation and task models originating from business policy is demonstrated and finally we will show the way knowledge-based components in function of applications are generated automatically.

This paper describes the possibilities of the translation of legislation, which is written in natural language, into a formal language, i.e. UML/OCL. The tool OPAL (Object-oriented Parsing and Analysis of Legislation) is developed to support the automatic modelling of legislation with the use of appropriate NLP techniques. The aim is not to perform this modelling in a batch fashion from legislation to final model, but interactively in dialogue with the knowledge engineer. The main components of OPAL are a parser (based on a chart-parser algorithm) and a model generator. A special component called modelling interface is added to OPAL to give the knowledge engineer the possibility to keep track of the modelling process and to make adjustments to the final model.