D5.5

Grant Agreement nº: 690974 Project Acronym: MIREL Project Title: MIning and REasoning with Legal texts Website: http://www.mirelproject.eu/ Contractual delivery date: 31/10/2019 Actual delivery date: 31/10/2019 Contributing WP 5 Dissemination level: Confidential Deliverable leader: APIS Contributors: APIS, UL

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690974

MIREL- 690974 Page 2 of 36 14/10/2019

D5.2

Final exploitation plan

Document History Version Date Author Partner Description

0.1 10/04/2017 Hristo Konstantinov APIS Initial draft 0.2 26/04/2017 Livio Robaldo UL Final draft 1.0 02/5/2017 Hristo Konstantinov APIS Final Version

Contributors Partner Name Role Contribution APIS Hristo Konstantinov Coordinator of

the deliverable Coordinating the writing of the deliverable; Interacting with the other partners to collect data.

UL Livio Robaldo Editor Editing many sections of the deliverable; Interacting with the other partners to collect data.

Disclaimer: The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any particular purpose. MIREL consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law.

MIREL- 690974 Page 3 of 36 14/10/2019

D5.2

Final exploitation plan

Table of Contents Executive Summary ............................................................................................................................. 4

1 Introduction ................................................................................................................................. 4

2 Industrial exploitation ................................................................................................................. 5

2.1 APIS.......................................................................................................................................... 5

2.2 Nomotika ................................................................................................................................. 6

2.3 DLVSystem ............................................................................................................................. 10

3 Funded follow-up projects ........................................................................................................ 15

3.1 ERC project CompuLaw ......................................................................................................... 15

3.2 Innovative Training Network LAST-JD (RoIE) ......................................................................... 16

3.3 LAILA ...................................................................................................................................... 17

3.4 InterLex .................................................................................................................................. 19

3.5 CrossJustice ........................................................................................................................... 20

3.6 SMEDATA ............................................................................................................................... 21

3.7 CREEP .................................................................................................................................... 22

3.8 CONFIRMA ............................................................................................................................. 23

3.9 WOODIe ................................................................................................................................ 25

3.10 CO-CITY .................................................................................................................................. 27

3.11 CO3 ........................................................................................................................................ 29

3.12 SANKOFA ............................................................................................................................... 31

3.13 Zhejiang University - University of Luxembourg Joint Laboratory on AIs, Robotics and Reasoning (ZLAIRE) ............................................................................................................................ 33

References ......................................................................................................................................... 35

MIREL- 690974 Page 4 of 36 14/10/2019

D5.2

Final exploitation plan

Executive Summary This deliverable lists exploitation solutions achieved with the research and networking activities of the project MIREL. The secondments in MIREL were devoted to exchange knowledge and expertise between the partners fit to develop joint theoretical research. The research has led to (a) devise computational solutions which can be used in the legal informatics market by the three industrial partners of the consortium, (b) prepare and submitting new joint research project proposals. This deliverables describes how MIREL activities affected the evolution of the market solutions offered by each of the three industrial partners and the follow-up research projects submitted by the partners in the consortium in 2018-2019 and retained for funding.

1 Introduction The secondments in MIREL were devoted to exchange knowledge and expertise between the partners fit to (a) develop joint theoretical research, (b) devising computational solutions which can be used in the legal informatics market by the three industrial partners of the consortium, (c) defining, preparing, and submitting new joint research project proposals. The present deliverable focuses in particular on points (b) and (c). The deliverable is structured in two main sections, respectively devoted to describe industrial exploitation and projects that have been retained for funding in the last biennium by MIREL partners and that may be considered as follow-ups of MIREL (in the sense that the research and technologies investigated in MIREL will be exploited therein). The section about industrial exploitation concerns the three industrial partners of MIREL: APIS EOOD, Nomotika SRL, and DLVSystem SRL. MIREL partners consider industrial exploitation of the research results crucial for the advancement in legal informatics, therefore lot of efforts have been devoted to investigate how the research achievements of the academic partners of MIREL can be used to enhance the performance of the products of the three companies and to devise new projects involving the partners. The section includes three subsection, one for each of the three industrial partners. On the other hand, since most MIREL partners are universities or research centers, we consider as exploitation the definition of new research projects built on the top of MIREL results, and possibly involving other non-MIREL partners, which has been involved through the MIREL network and/or the events organized in the context of the project (conferences, tutorials, etc.). Section 3 is then devoted to briefly describe the projects retained for funding by at least one MIREL partner. Several projects have been retained for funding indeed, from various funding authorities, first of all the H2020, including an ERC project and an Marie Curie Innovative Training Network project.

MIREL- 690974 Page 5 of 36 14/10/2019

D5.2

Final exploitation plan

2 Industrial exploitation

2.1 APIS APIS is a leading legal and business information provider in Bulgaria. In recent years, the company strategy is focused on development of innovative legal information products and services related to the application of EU law. At the end of 2015, the company launched the EuroCases1 online system. This service provides access to EU legislation and case law as well as case law of Member States’ courts that is linked to EU law. EuroCases service is built upon APIS’ achievements within the FP7 project EUCases2, in which they participated together with the MIREL partners UNITO, Nomotika, and UNIBO (the latter as subcontractor). Within MIREL project, APIS aimed at enhancing knowledge and expertise in NLP technologies in collaboration with researchers of UNITO as a hosting organisation. APIS developers completed all the planned five one-month secondments in Turin in the first year of MIREL project. Thanks to successful teamwork, the existing NLP tools used in EuroCases service for Named Entity Recognition (NER) of legal citations in EU legislative and judicial documents have been significantly improved. Their functionality was extended with recognition of legal citations in Italian, which was subsequently implemented in the legal repositories developed within the ongoing projects InterLex and SMEDATA (see sections 3.4 and 3.6 below). The tools for recognition of legal citations to EU law were made available as a web service (REST API) to the partners UNITO and Nomotika for implementation further of NLP tasks. Their work was facilitated also by the developed tool for automated extraction and conversion of EU legislative instruments in XML-documents with a simplified structure. It was used to facilitate and make quicker the performance of the algorithms for detection of similarities between legal texts. Especially successful was the cooperation between APIS developers and Rohan Nanda, a PhD student of UNITO. The core topic of his PhD thesis, which he defended in March 2019, was related to the automated recognition of national legislative measures implementing the provisions of EU directives. APIS has strongly supported his research activities by ensuring the needed multilingual legal data in simplified XML format and providing tools for recognition of legal citations and a web-based interface for visualisation in a table of comparison of similar provisions of normative acts. The knowledge and expertise in NLP technologies gained as a result of the teamwork with UNITO researchers were further exploited in a number of improvements, which were implemented afterwards in the existing products and services of APIS:

• Recognition of names of companies and other legal entities in legislative, administrative and judicial documents and establishment of links to the company database of APIS. This functionality, together with the recognition of legal citations

1 http://eurocases.eu/ 2 http://eucases.eu

MIREL- 690974 Page 6 of 36 14/10/2019

D5.2

Final exploitation plan

to EU law, was integrated in the so-called Link Detector3 – a free extention to most popular browsers and MS Word;

• Recogntion of names of natural persons in Bulgarian case law documents and their automated anonymisation in compliance with EU data protection requirements;

• Recognition of Bulgarian toponymes in legal texts and establishment of external links to their location in Google Maps;

• Recognition of citations to judgments of other court in Bulgarian case law documents, which in fact is a very complicated NLP task due to the lack of a national case law identifier in Bulgaria. In addition, the existence of an almost infinite variety of ways of citing court decisions is usually combined with a lack of sufficient attributes in the referring text allowing the unequivocal identification of the cited judgment;

• Improvement of the existing algorithms for text similarity by recognition of keywords, legal definitions and legal citations;

• Enhansement of the existing tools for recognition of legal citations to EU and Bulgarian law with options to identify citations to the most important national legislative instruments of Germany, Austria, France and Italy in the field of private international law and personal data protection law;

• Development of an NLP-based algorithm for the so-called “Did you mean” search engine function in APIS online product platform that scans for potential spelling or grammatical errors in user queries and recommends alternative keywords;

• Development of a tool for automated classification of Bulgarian court decisions (still in experimental phase) based on recognition of keywords, legal definitions and legal citations as well as on the identification of similarity with the provisions of legislative instruments.

Many of the above listed NLP tools have been used also in the follow-up projects with APIS involvement (see sections 3.4 – 3.6 below).

2.2 Nomotika Nomotika s.r.l is a spin-off of UNITO researching and developing cutting-edge ICT solutions for the daily work of legal practitioners. The main product of the company is the MenslegiS system4, which is the commercial version of Eunomos [Boella et al, 2016], an advanced legal document management system, developed in the past years via a collaboration between UNITO and UL, that integrates NLP procedures and XML legal standards for the management of legal texts for regulatory compliance.

3 https://apis.bg/en/link-detector-en 4 http://www.nomotika.it/EN/MensLegis/Flyer

MIREL- 690974 Page 7 of 36 14/10/2019

D5.2

Final exploitation plan

During second #104 and #121, Serena Villata and Elena Cabrio from INRIA worked together with Nomotika S.r.l. to investigate possible evolutions of the MenslegiS system through comparisons with the similar tools NLL2RDF5 and Licentia6, developed in the past by INRIA together with the University of Cordoba. During the secondments has been discussed the need of (1) a more standard axiomatic ontology written in Ontology Web Language (OWL) within MenslegiS, which currently uses the lightweight ontology European Legal Taxonomy Syllabus [Ajani et al., 2017] and of (2) annotations of the legal documents within MenslegiS in the Akoma Ntoso XML standard; these documents have been originally annotated in the NormeInRete standard 7, an obsolete XML markup language, which is suitable to handle Italian legislation only.

In light of these discussions, it has been decided to develop a demo of MenslegiS employing Akoma Ntoso annotations, a sample ontology in OWL, and Semantic Media Wiki (SMW)8 as underlying framework. SMW is a free, open-source extension of software that powers Wikipedia, designed to favor the integration in the system of legal ontologies and modern machine learning techniques to populate them. SMW allows to store and query data within the wiki’s pages. All data created within SMW can easily be published via the Semantic Web, thus facilitating the integration with other systems. Furthermore, many spin-off extensions are available for SMW, which can allow the building a powerful and flexible knowledge management system. Serena Villata gave a tutorial during her secondments to Nomotika s.r.l. in order to illustrate the functionality of SMW and its interface with the OWL language. Several researchers working at UNITO and UL, as well as programmers and legal experts of Nomotika, have been invited to the tutorial.

The demo has been implemented by Livio Robaldo, working at the University of Luxembourg and stakeholder of Nomotika s.r.l., during secondment #98 and part of secondments #56 and #88. Figure 1 and Figure 2 show two screenshot of the MenlegiS demo in SMW, developed by Livio Robaldo during the mentioned MIREL secondments.

5 http://www.airpedia.org/nll2rdf/ 6 http://licentia.inria.fr 7 http://www3.cirsfid.unibo.it/didattica/upload/46_NormeInRete.pdf 8 https://www.semantic-mediawiki.org

MIREL- 690974 Page 8 of 36 14/10/2019

D5.2

Final exploitation plan

Figure 1 - screenshot of MenslegiS demo in SMW

On parallel, Livio Robaldo investigated, together with Giovanni Siragusa working in UNITO and Andrea Violato working in Nomotika s.r.l., the design and implementation of a system for Recognizing Textual Entailment (RTE) in the cybersecurity domain, to be possibly implemented in the future as a module of the MenslegiS system.

The idea of implementing an RTE module for MenslegiS stemmed after discussions with prof. Cleo Condoravdi, working at Stanford University, which Livio Robaldo and Giovanni Siragusa were visiting during (part of) secondment #56 and (part of) secondment #58. Prof. Cleo Condoravdi has worked in the past in textual entailment, and, although she did not wish to have a direct role in the proposed research, she provided Livio Robaldo and Giovanni Siragusa precious insights and directions during their visit at Stanford University.

RTE is at the base of many NLP tasks, ranging from information retrieval to common sense reasoning. RTE is the specific task of recognizing the relation between two sentences, in order to measure whether and to what extent one of the two is inferred from the other: given two sentences, where the first one is called premise and the second one hypothesis, the aim of RTE is to asses if: (i) premise contradicts hypothesis (i.e., premise contradicts information present in hypothesis), (ii) premise is not related to hypothesis (also known as neutral relation), or (iii) premise entails the hypothesis.

MIREL- 690974 Page 9 of 36 14/10/2019

D5.2 Final exploitation plan

Figure 2 - screenshot of MenslegiS demo in SMW

In the legal domain, from a set of obligations that are known to be complied with, RTE may be used to infer which other norms are complied with as well. RTE allows then to find entailments between rules, retrieving more complete and accurate information for legal experts (e.g., judges or lawyers).

The cyber security domain provides real use cases for RTE applications. This domain is also very relevant for Nomotika s.r.l., in that the company collaborates with consultants in the field. In cyber security there exist guidelines that describes standard procedures that a company has to implement to protect their data. Such procedures are generally defined by ISO 9 (the International Organization for Standardization) and described in details (or entailed) by NIST10 (National Institute of Standards and Technology).

Livio Robaldo, Giovanni Siragusa, and Andrea Violato jointly developed:

• Two datasets for RTE regarding cyber security. Those datasets have been built by usingrelations between guidelines (i.e., a guideline could cover or be related to another one).Those relations are defined by legal experts when they create those guidelines;

• An evaluation of three classifiers on our datasets. In details, two Maximum Entropyclassifier and a Neural Network model have been tested. Furthermore, since there not

9 https://www.iso.org/home.html 10 https://www.nist.gov/

MIREL- 690974 Page 10 of 36 14/10/2019

D5.2 Final exploitation plan

exist trainset for legal RTE, those classifier have been trained on Stanford Natural Language Inference (SNLI) dataset [Bowman et al., 2015] which contains more then 500,000 training instances.

2.3 DLVSystem DLVSystem s.r.l. is a spin-off company of University of Calabria. The main product of the company is DLV, which is still acknowledged as one of the most powerful Answer Set Programming (ASP) systems and the most widely used in commercial applications. DLVSystem holds the intellectual property of DLV-based solutions and prototypes and has the right of licensing all of its implementations and commercial solutions.

Within the MIREL project, DLVSystem aims at widening the application of ASP and DLV. In particular, the company is supporting research activities related to reasoning about legal domains, providing specific technologies based on a long-time expertise in the design, development and engineering of advanced logic-based Artificial Intelligence (AI) and Knowledge Representation and Reasoning (KRR) solutions.

Reasoning with ASP about massive data coming from legal domains is an extremely challenging task. Indeed, state-of-the-art ASP systems and ASP reasoning procedures were not conceived having the challenges of Big Data in mind, thus they are not applicable tout court in this new setting. The need for processing Big Bata repositories implies a technological switch including the development of new hardware and software architectures to handle the exponentially growing quantity of data [Antoniou et al, 2018].

During visits by HUD researchers, in the first year of the project, DLVSystem got new knowledge on topics related to Big Data processing. During these secondments, researchers from DLVSystem started working together with the MIREL partners (HUD, in particular) to move the first step towards enabling the specification of reasoning tasks on Big Data with ASP. Our ongoing work aims at extending the DLV system to interact in a plausible way with Big Data repositories. In our proposal, DLV is connected to SQL-based data warehousing tools such as Hive via a translation component, that allows to demand the computation of space-demanding tasks/queries to well-assessed Big Data software, and leave

11 http://clic2019.di.uniba.it/ 12 https://lrec2020.lrec-conf.org/en/

MIREL- 690974 Page 11 of 36 14/10/2019

D5.2

Final exploitation plan

to the standard ASP engine the burden of evaluating more computationally complex decision-making tasks over a selection of the original data.

In the following, we first introduce a motivating example that will be used through the rest of this section to show the idea underlying our approach. Then, we provide some details about our technique, and design an architecture where we indicate the software components that can be used to realize our solution.

The following use case can be useful to grasp the intuition behind our approach. Let us consider the following scenario: Giovanni wants to go out for dinner and wants to invite a given number of people, possibly inviting friends of friends, in order to spend some funny and pleasant time. Invited people should be selected from a social network, choosing among the Giovanni’s friendship net. In such social network, subscribed people can add friends and mark some of these as close friends; moreover, the social network computes some stats on the degree of dislike among two people in the network. The following ASP program can be used to explore the friendship relations of such social network and then to provide Giovanni with suitable suggestions on people that could be invited.

nfriends(10). averageAge(25).

r1 possible_friend(Y) :– close_friend(giovanni,Y).

r2 possible_friend(X) :– possible_friend(Y), close_friend(Y,X).

r3 suggested_friend(Y,A) :– possible_friend(Y), person(Y,A), A > 18.

r4 invite(X) | -invite(X) :– suggested_friend(X).

r5 :– #count{X:invite(X)} != N, nfriends(N).

r6 :– #sum{A,X:invite(X), suggested_friend(X,A,_)} < AVG ∗ N,

nfriends(N), averageAge(AVG).

r7 :∼ invite(X), suggested_friend(X,_), dislike(giovanni,X,D). [D@1,X]

The former two rules compute the transitive closure of the friendship relation of Giovanni, restricting the computation to close friends only; rule r3 suggests a person Y if she is a possible friend and is older than 18; rule r4 guesses if a suggested friend should be invited or not; rule r5 ensures that the number of invited friends is exactly the desired one; r6 imposes that the average age of the invited friends is not smaller than a given value; eventually, rule r7 prefers solutions in which the total degree of dislike among Giovanni and invited people is minimized. Intuitively, computing the transitive closure of the friendship relation in a social network could be very expensive when performed on a huge database. Thus, traditional main memory ASP systems cannot handle it; indeed, also the simple import of the friendship relation is not feasible in practice. So, the idea is to delegate this heavy part of the computation to an external Big Data source. The remaining part instead can be conveniently evaluated by an ASP system given that, from a computational perspective, it is a NP-hard task that takes as input a heavily reduced part of the friendship net.

MIREL- 690974 Page 12 of 36 14/10/2019

D5.2

Final exploitation plan

In order to delegate the computation of data-intensive rules to Big Data tailored software, one can make use in the ASP program of a new external atom (an external atom is a special form for atoms supported by DLV geared towards external computations – the extension of a DLV external atom is specified via externally defined Python functions). Roughly, the external atom connects to a DB, may export some input facts to the DB, converts some given rules to queries, executes them on the DB and returns as output the extension of a specific predicate. In order to enable the external evaluation on a DB db, the ASP program should contain a rule r having in the body an external atom called &bigasp, that receives as input:

• db, the name of the ODBC DSN (Data Source Name);

• user, the name of the user who connects to the DB;

• password, the password of that user;

• rules, a string containing some Datalog rules that define the operations that will be performed on the DB;

• output, a string representing the name of the relation to be imported in the ASP program;

• input [optional], a string containing a set of ASP facts that have to be exported to the DB.

During the evaluation, DLV invokes the external atom, imports the results of the evaluation and fills in the extension of the predicate output that has to appear as head in the rule r. Note that, this approach could be adopted to retrieve data from diverse Big Data sources.

Let us consider again our running example. We can delegate the computation of the transitive closure of the friendship relation to an external Big Data platform by replacing the first three rules in the program above (r1 - r3) with the following one:

suggested_friend(Y,A) :– &bigasp(“db”, “user”, “pwd”, “

possible friend(Y) :– close friend(giovanni,Y).

possible friend(X) :– possible friend(Y), close friend(Y,X).

suggested friend(Y,A) :– possible friend(Y), person(Y,A), A > 18.”,

“suggested friend”; Y, A).

Apart from connection parameters and the name of the output relation, the external atom takes as input the extracted rules; it invokes the machinery for enabling their evaluation on the external db and returns the results as a sequence of tuples representing the suggested friends with their age. Such tuples populate the extension of the suggested_friend relation so that the traditional ASP evaluation can continue.

The general architecture of a framework for reasoning on Big Data with ASP can be therefore composed by 3 macro-components, as illustrated in Figure 3. In particular, the data layer of

MIREL- 690974 Page 13 of 36 14/10/2019

D5.2

Final exploitation plan

the system could rely on Hive13, a data warehouse software project built on top of Hadoop14 for providing data summarization, query and analysis. The choice of this tool is due to the fact that Hive is the mainstream for OLAP analysis over Big Data. Hive brings three main advantages: (i) it exploits the computational power of Hadoop clusters, (ii) it implements a standard relational model, that is equivalent to the data model supported by ASP, and (iii) it supports a querying language called H-QL, which is very close to the standard SQL. On top of Hive we find BigDLV, the core of our framework for reasoning on Big Data. BigDLV should allow for reasoning in ASP over data stored and pre-processed within Hive. In particular, BigDLV should be able to extract extensions of input predicates from the underlying Big Data repository and, moreover, delegate parts of the computation to Hive. Figure 3 shows the internal architecture of BigDLV and focuses possible interactions with the underlying layers. This component encapsulates the DLV system which allows to define external atoms as discussed above: when an input program embeds a Datalog rule to be evaluated against the Hive repository, DLV, by means of an external atom, invokes a Python component, called DataSQL, managing the interaction between DLV and Hive. DataSQL is in turn composed by two sub-modules, the Compiler and the Executor. The former compiles the rules received from DLV into an SQL query, while the latter runs the compiled query over Hive and retrieves the result. Results from Hive are first filtered out by the Executor and then returned back to DLV. An end-user could ask the framework to store the final output of the whole evaluation process into the cluster. Note that, the Compiler module interacts directly with Hive in order to get information about table schemata matching the rule predicates. The implementation of the Compiler can follow the lines of (Terracina, 2008) and thus supporting only stratified Datalog programs. Communication between DataSQL and Hive can be performed via PyHive15, a python module providing a collection of python DB-API16 interfaces for Hive.

13 http://hive.apache.org/ 14 http://hadoop.apache.org/ 15 https://github.com/dropbox/PyHive 16 https://www.python.org/dev/peps/pep-0249

MIREL- 690974 Page 14 of 36 14/10/2019

D5.2

Final exploitation plan

Figure 3 - General architecture of the framework for ASP reasoning over Big Data

Currently, we are implementing all the components mentioned above. Moreover, we are developing a case of study that combines a Datalog sub-programs modeling a heavy data-mining task and whose evaluation is demanded to the Big Data platform, with another component that models a computationally hard decision-making task to be evaluated by an ASP system.

MIREL- 690974 Page 15 of 36 14/10/2019

D5.2

Final exploitation plan

3 Funded follow-up projects

3.1 ERC project CompuLaw Prof. Giovanni Sartor, one of the main authorities in MIREL, co-author of deliverables D1.1, D1.2, D1.3, and D1.4 and president of the European Doctoral School in Law, Science, and Technology (LAST-JD, see section 3.2 below) was awarded an ERC awards Advanced Grant for the project "CompuLaw"17.

The project CompuLaw addresses the regulation of computations (processes and systems) through an innovative integrated legal and technological framework: it provides epistemic, technical and normative guidance for the sound development of computable law and law compliant computations.

The context for my project is given by the ongoing transformation of our social world into a hybrid infosphere, populated not only by humans, but also by a huge and growing number of increasingly pervasive, autonomous and intelligent computational entities. Such entities have become an essential aspect of today's social ecology: we work, play, learn and communicate by symbiotically interacting with computations, and depend on them for most aspects of our social fabric (production, logistics, administration, transport, communications, services, etc.).

As computations highly contribute to the realisation of valuable collective individual interests, they also cause serious risks. Unless computational entities are well-behaved and comply with legal norms, the infosphere may become an unfriendly and dehumanising environment, where individuals are subject to persistent surveillance and to determinations over which they have no control, being outsmarted, cheated, and possibly harmed by faulty, illegal or unethical computations.

Opportunities and risks are magnified by the advent of Artificial Intelligence (AI). AI entities differ from previous mechanical devices, since they engage in complex cognitive tasks (pattern recognition, inference, planning, decision-making, etc.), and adapt to variable contexts, possibly learning from experience. Endowing computational entities with high-level cognitive capacities, AI increases their ability for diversified and autonomous action, both in physical domains (robotics) and in virtual ones (digital bots, intelligent agents).

On the one hand, AI systems may enhance human abilities, improve security and efficiency, and enable the universal provision of knowledge and skills. On the other hand, they may increase opportunities for control, manipulation, and discrimination; disrupt social interactions; and expose humans to harm resulting from technological failures or disregard for individual rights and social values.

17 See https://www.eui.eu/ServicesAndAdmin/CommunicationsService/News/2019/EUI-Law-Professor-Giovanni-Sartor-wins-ERC-grant

MIREL- 690974 Page 16 of 36 14/10/2019

D5.2

Final exploitation plan

To effectively achieve these objectives, the law must become computable, i.e. it must also be an internal component of computational processes rather than an external constraint over them. Legal norms, values and principles must be mapped onto, and partially translated into computable representations of legal information and reasoning, which are processed by computational entities.

The aim of the CompuLaw project is to provide evidence-based and critical support, informed by both legal theory and computer science, to computable law, i.e., to the computable specification of legal requirements, and the engineering of Artificial Legal Agents able to comply with such requirements. The achievement of this objectives will encompass different crucial aspects:

− What legal requirements in what domains and to what extent should be made computable;

− What technologies and architecture should be used;

− How to combine top-down compliance with predefined rules and learning from cases;

− How to addresses normative conflicts;

− How to apply rules according to legal values/principles;

− What formal/substantive legal constraints should govern the design and deployment Artificial Legal Agents.

CompuLaw views computable law as a human enterprise, over which society should maintain full ownership and control. Thus, the project also addresses the socio-technical conditions for the correct design, deployment and monitoring law compliant computations.

The project adopts a highly interdisciplinary perspective, combining three disciplinary domains: a legal-social cluster, a philosophical-logical cluster, and a computing-AI cluster.

3.2 Innovative Training Network LAST-JD (RoIE) The project “LAST-JD: Right of the Internet of Everything (RoIE)” has been retained for funding by the European Commission under the H2020 Marie Curie Innovative Training Network, for the year 2018. The consortium of the project involves nine European universities, among which the MIREL partners UNIBO (in the role of coordinator), UNITO, and UL, and fifteen more external partners (companies and non-European universities), among which the MIREL partners APIS EOOD and Nomotika s.r.l.

LAST-JD, the single European Doctoral program in Law, Science, and Technology, was founded in 2012; UNIBO, UNITO, and UL are among the six founder universities. Since 2012, the school has hired about fifty PhD students in legal informatics, many of which have spent visiting research periods in UNIBO, UNITO, and UL. The LAST-JD program was one

MIREL- 690974 Page 17 of 36 14/10/2019

D5.2

Final exploitation plan

of the main reason why MIREL was proposed, i.e., to extend the networking activities of the PhD students beyond the European level; in 2018-2019, several LAST-JD students visited non-EU MIREL partners, e.g., Stanford University.

LAST-JD (RIoE) is intended to define an international joint doctorate educational training (15 fellowships) to create a common platform of knowledge and language for early stage researchers working in Law, Science and Technology and specializing in Internet of Everything (IoE) and to establish structures for long-term cooperation, strengthening relationships among the leading universities, research institutes and enterprises and to continuously develop the research training platform that European industries rely on.

The 15 PhD students will be trained in a joint academic/industrial program with a common training plan, cutting-edge training-by-research, high quality supervision, complementary and transferable skills training, summer schools, etc., in order to pursue an innovative research project that will tackle a timely and important scientific problem with an interdisciplinary approach (Computer Science, Law and Ethics) and to transfer expertise/know-how among the partners of the Consortium and with external groups via industrial internships, networking activities, inter-sectorial exposure, secondments, workshops, sharing of learning material, public engagement and outreach activities.

Doctorate projects will be jointly supervised within a joint governance structure in which the beneficiaries as well as the partner organizations will be actively involved. The doctorate program will expose PhDs to different sectors and they will acquire a comprehensive set of transferable skills. The projects are designed to enhance collaboration and interaction across these disciplines, to integrate non-academic partner organizations, and to develop interdisciplinary methodologies for building, studying and regulating IoE. It will create a generation of young researchers trained in the above topics. They will end up with the knowledge and entrepreneurial capacity to create innovative companies or work in research, industry or public administration.

3.3 LAILA The project (LAILA: Legal Analytics for Italian LAw) has been retained for funding by the Italian Ministry of Education and Research under the “PRIN: PROGETTI DI RICERCA DI RILEVANTE INTERESSE NAZIONALE” schema. The principal investigator of the project is Giovanni Sartor, from the University of Bologna. The project aims at applying recent advancements in legal informatics, also investigated in MIREL, to Italian legislation.

The project LAILA addresses the application of methods of legal analytics to a vast and diverse set of legal information: legislation, case law, and empirical legal data. It studies the use of analytics—a mix of data science, Artificial Intelligence, Machine Learning, Natural Language Processing, and statistics in the legal domain, to extract legal knowledge, infer undiscovered relationships, and engage in data-driven predictions.

MIREL- 690974 Page 18 of 36 14/10/2019

D5.2

Final exploitation plan

LAILA is premised on the data-and-AI paradigm shift that has taken place in the last two decades: AI technologies, as applied to vast datasets (big data), have already transformed many aspects of our economic and social life, delivering many useful applications, but also causing some serious drawbacks (surveillance, monopoly power, manipulation of individual and social choices, etc.).

The study and the practice of the law are also being affected as new powerful methods become available for legal cognition and practice. In this context, LAILA will aim to place Italian legal research and practice at the forefront in the use of legal analytics, by testing and developing technologies, providing ethical and legal guidance, and delivering new insights on the functioning and evolution of law. LAILA's contribution is crucial to this objective: while vast repertoires of digital resources on Italian law are already available; on the other hand, tools and methods for adequately processing such resources are still missing.

LAILA has three main objectives:

1. To apply, refine, and develop technologies for LA. This includes:

a) Applying analytics technologies—including supervised, semi-supervised, and unsupervised learning—for the following purposes: ontology building, classification of legal documents, analysis of legislation, analysis of case law, extraction of “massime” (rationes) and principles, question-answering, and prediction of trends and decisions. Test applications, and corresponding evaluations, will concern large sets of legal sources, and selections of socio-legal data, including Italian legislation, the case law of the Court of Cassation and lower courts, decisions of the Italian Data Protection Authority (Garante), parties’ documents produced in legal cases, and the corresponding socio-legal data.

b) Refining and developing legal analytics technologies, taking into account the peculiarities of the Italian legal system and language. In particular, we shall combine legal analytics on English and Italian texts, using existing multilingual repositories, apply legal analytics to annotated legal document, and combine legal analytics methods with the logical representation of legal knowledge.

2. To provide methodological analyses and guidelines for the efficient and ethical deployment of legal analytics technologies. This includes:

a) Reviewing the capabilities and limitations of current legal analytics technologies and identifying risks and mitigations.

b) Providing ethical and legal guidelines, specifying how to deploy legal analytics for different purposes (classification, analysis, prediction, etc.), while complying with law and ethics and preventing any risks of adverse effects (e.g., biases).

3. To expand the understanding of the structure, logic, and dynamic of Italian law in its connection with EU law, using legal analytics tools. This includes:

MIREL- 690974 Page 19 of 36 14/10/2019

D5.2

Final exploitation plan

a) The linguistic, conceptual, and logical structures of Italian legislation and case law, and of their evolution.

b) The network of multiple connections between different legal sources, of the resulting emerging influences, constraints, commonalities, and differences, in a multilevel framework (also including EU law).

c) The correlations between social factors and legal sources and decisions.

LAILA has the potential to have a number of applications, through successful implementations by public and private actors, and a strong impact on science and technology as well as on society and economics:

− Scientific and technological impact. LAILA will have an impact on science and technology both directly—by producing innovative legal analytics methods, technologies and applications—and indirectly, by stimulating legal analytics research and applications.

− Socioeconomic impact. LAILA will a positive impact on the effectiveness and efficacy of the Italian judiciary and on the competitiveness of Italian lawyers and legal-technology developers. LAILA’s project team combines all the different scientific and methodological approaches into a new interdisciplinary synthesis combining law, computing, legal informatics, artificial intelligence, machine learning, legal theory, and computer ethics.

3.4 InterLex The partners APIS, UNIBO and UNITO are currently involved in InterLex (http://www.interlexproject.eu/), a two-year project (01.09.2018 – 31.08.2020) funded under the EU’s Justice Programme. The project aims at developing an online platform to provide information, decision support and training on Internet-related private international law. It addresses the identification of the legal system having jurisdiction and of the national law to be applied to a case, as well as the retrieval of relevant legal materials. The platform comprises the following three modules:

• The Decision Support Module is an interactive expert module that offers users a guided tour, according the relevant rules of legislation and case law, to determine jurisdiction and/or applicable law in a specific Internet law case involving an international element. The architecture of the system is based on a knowledge base of rules, modelling legislative prescriptions and on a reasoning module that applies the rules to specific cases, and also provides links to the relevant sources.

• The Find Law Module supports users in retrieving legal information in a large collection of European, national and international instruments and case law in the area of Internet-related private international law as well as commentaries of renowned

MIREL- 690974 Page 20 of 36 14/10/2019

D5.2

Final exploitation plan

experts, journal articles, guidelines, authorial notes and other theoretical and practical materials.

• The Training Module includes a set of training tools on European private international law, to be used in taught courses as well as in self-training of students and magistrates. It includes both the interactive visualisation of content and practical exercises. The module provides lecturers and tutors with the necessary functional tools and interactive environment for publishing and interlinking their own training materials, such as articles, comments, lectures, guidelines, answers to questions, sample tests, etc.

Demo versions of the Decision Support and Find Law Modules have been launched recently on the InterLex Portal (https://interlex-portal.eu).

Using the expertise in NLP techniques gained during the secondments in Turin in the first project year, APIS developers have extended their knowledge to identify complex structures of norms as well as citations to EU legislation and case law in the Italian legal texts included in the Find Law Module. In addition, APIS is working on improvements of the multilingual search interface of the module by developing and integrating a prototype demonstrator of a cross-lingual search functionality in Italian and Bulgarian languages.

The partner UNIBO, who is participating in the project through CIRSFID, a centre for research on Legal Informatics, supported by UNITO as project coordinator, plays the crucial role for encoding EU private intrernation law rules and their application in meaningful representations, enabling automatic reasoning in the Decision Support Module.

3.5 CrossJustice APIS, UNIBO and UNITO are collaborating also in the recently started CrossJustice project, a two-year action (01.09.2019 – 31.08.2021) supported by the EU’s Justice Programme. The project aims at developing an online platform for advice and support on the effectiveness of procedural rights of accused or suspected of a crime providing a free service, mainly directed to legal professionals, but accessible to law students, NGOs and all EU citizens. The platform will address both the compliance with the EU acquis on procedural rights for accused and suspect of crimes and the compatibility of procedural rights among different national legal orders, as well as the retrieval of relevant legal materials. To achieve its purpose, the project will deliver (1) a free of charge and updated information and advisory service, and (2) capacity building for legal professionals and law students.

The CrossJustice platform will include two modules: LegalDataBase Module and Advisory Module. The LegalDataBase Module supports the user in retrieving legal information on procedural rights. It provides access to a large collection of European and national legislation and case law on the matters. The selection of relevant data is part of the legal research activities to be performed by project partners. The database will be updated regularly during

MIREL- 690974 Page 21 of 36 14/10/2019

D5.2

Final exploitation plan

the project. In order to support advanced semantic research (conceptual retrieval) functionalities, legal information will be annotated with regard to main legal concepts, actors, and facts. To this end, the project will develop a formal and computable ontology for the domain of criminal procedural law, including the definition of the relevant categories, properties, and relations between the main concepts of the domain. In developing the ontology, the project will also explore methods to put in relation concepts coming from different legal systems and expressed in different languages. The Advisory Module is an interactive expert module that supports experts in identifying and applying relevant rules of EU and national legislations concerning procedural rights of persons suspected or accused of crimes, both at domestic and cross-border level. In particular, the module will (1) assess the compliance of national implementing instruments with the EU acquis and highlight potential gaps in the implementation process, and (2) examine the compatibility between national frameworks, thus identifying potential gaps and inconsistencies for the procedural right of accused or suspected that could hamper horizontal cooperation.

3.6 SMEDATA The partner APIS is involved in the ongoing SMEDATA project (https://smedata.eu/), funded under the EU’s Rights, Equality and Citizenship Programme. The overall objective of this two-year project (01.12.2018 – 30.11.2020) is to ensure the effective application of the General Data Protection Regulation (GDPR) through awareness, multiplying training and sustainable capacity building for micro, small and medium-sized enterprises (SMEs), and for legal professionals. To reach this ambitious goal, the partners will strive to achieve the following underlying objectives:

• To elaborate a methodology for designing self-assessment and awareness tool, thus providing a sustainable approach for their implementation;

• To assist the understanding and compliance with GDPR through the development of an innovative mobile application.

The project is aimed at two main target groups – SMEs and legal professionals, as well as a subsidiary one – citizens in their role as data subjects.

The partner APIS is responsible for the development of the mobile application “GDPR in your pocket”. Its beta version was recently launched in the app stores of Google and Apple. The purpose of the mobile application is to introduce GDPR to citizens and SMEs in an easy-to-use and easy-to-understand way, and to give them practical knowledge and advice with respect to their rights / obligations under the new EU data protection law. The application provides access to a variety of legal information resources and guidance materials in the field of privacy and personal data protection organised in two main components – Legal library and Advisory module. The Legal library offers access to a rich collection of legal documents of the European Union and two EU Member States – Bulgaria and Italy, whereas the

MIREL- 690974 Page 22 of 36 14/10/2019

D5.2

Final exploitation plan

Advisory module provides practical guidance on GDPR rules to citizens and SMEs. The user interface and large parts of the content are available in English, Italian and Bulgarian.

3.7 CREEP The partner INRIA is involved in the CREEP (Cyberbullying Effects Prevention), funded under the Digital Wellbeing Action Line of EIT Digital. Researchers at INRIA will use their expertise on argumentation and argument mining they also built in the context of MIREL secondments, in particular the ones to/from University of Cordoba.

The project CREEP aims at identifying and preventing the possible negative impacts of cyberbullying on young people. It seeks to realise advanced technologies for the early detection of cyberbullying phenomena through the monitoring of social media and the communication of preventive advices and personalized recommendations tailored to adolescents’ needs through a virtual coaching system (chatbot). The technology developed in the project is intended for the use of students, teachers and parents, as well as to all operators in the field of education and health that act at the local level to prevent and combat cyberbullying. The partners of the project are Fondazione Bruno Kessler (IT), Universita di Trento (IT), INRIA (FR), Expert system (IT), and Neuronation (DE).

Thanks also to the support of the Department of Health and Social Solidarity and the Department of Knowledge of the Autonomous Province of Trento (Italy), Trentino region will become a living lab where cyberbullying will be analyzed not only on the Web, but also through a survey administered to a representative sample of students and in-depth interviews with experts in the fields of education and health. In addition, students and teachers of some pilot classes of local middle and high schools will take part in educational and exploratory workshops, aimed at raising awareness on the phenomenon and testing the technological tools developed.

The purpose of CREEP is to provide a set of tools to support the detection and prevention of psychological/behavioral problems of cyberbullying teenage victims. As said above, the objective is achieved combining social media monitoring and motivational technologies (virtual coaches integrating chatbot). Starting from February 2018, two research engineers have been hired in INRIA (Pinar Arslan and Michele Corazza), and supervised by Serena Villata and Elena Cabrio, who implemented several MIREL secondments. The two research engineers are currently working on mining arguments where cyberbullism features are identified.

MIREL- 690974 Page 23 of 36 14/10/2019

D5.2

Final exploitation plan

3.8 CONFIRMA The partner INRIA is involved in the national project CONFIRMA (COntre-argumentation contre les Fausses InfoRMAtions), funded by the Direction Générale de l'Armement (DGA), under the Régime d'Appui pour l'Innovation Duale (RAPID) program.

Similarly to the project CREEP described above, researchers at INRIA will use in this project their expertise on argumentation and argument mining, in order to devise IT techniques employing counter-argumentation against fake news. As it is well known, the identification of fake news is a topic of special interest nowadays given its impact on the society. The partners of this project are Storyzy, INRIA, and Institut Jean Nicod.

The CONFIRMA project aims at fighting fake news by tackling their creation and their diffusion as well as identifying the best strategies of response. It will build on the results of the VerDi project, while integrating recent advances in counter-argumentation, investigated within the MIREL project.

The VerDi project developed a framework in which false information and omissions in Internet were automatically identified, in order to:

• Better characterize fake news (more refined textual analyzes, detection of the reuse of data, as well as recognizing old images, performing chronological analyses, etc.);

• Identify the communities that share these fake news in social networks;

• Propose methods and tools to maximize the effectiveness of counter-arguments to communities.

Currently, the partner Storyzy is able to automatically detect new fake news sites (extremists, propagandists, conspiracy, etc.) shared on social networks. Storyzy is the first independent tech company that detects and classifies fake news sources (false information, propaganda, conspiracy...) via its 100% automated content technology.

Storyzy continuously crawl news sites or blogs and searches for keywords that appear frequently in its fake news site database to find URLs belonging to potential new sites. These sites are then analyzed in detail with a technology termed Automatic Language Processing (TAL) in order to extract particular reported speeches (citations) to determine if they should be considered suspicious. The Storyzy database, which has been in existence since 2015, contains more than 35 million citations and is continuously enriched with the processing of more than 100,000 articles per day.

The central idea of the CONFIRMA project is to build a new tool to produce a graph that would make it possible to study the relationships between the users who share these fake news sites and the arguments that are conveyed to them.

MIREL- 690974 Page 24 of 36 14/10/2019

D5.2

Final exploitation plan

Using techniques of Automatic Language Processing and the theory of argumentation, CONFIRMA partners want to go beyond the identification of these communities by

• Proposing counter-arguments automatically extracted from other articles on the same topics as those that are shared by an identified community

• Helping to build semi-automatically counter-arguments in cases of informational warfare.

Several lines of work have been identified in order to contribute from a technological point of view to the success of the project:

• Identify communities that share URLs belonging to fake news sites. In this respect, it would be desirable to be able to perform fake news detection at the URL level and not only at the domain name of the site. That is, instead of considering a whole web domain name as distributing fake news, the goal would be to identify only the URLs that broadcast it. This feature would be especially useful on sites like English tabloid (The Daily Mail, The Sun, The Mirror, etc.) because some of their contents are reliable (people, sport, news) while others can be considered as fake news (used by extremists, propaganda promoters, etc.). So the challenge would be to identify only the people who share the URLs that really lead to articles using fake news.

• The proposed counter-argumentation must be tailored to target audiences, which requires a thorough characterization of potential audiences. In other words, a counter-argumentation is not uniform but must coincide with the receptivity of the interlocutor, more sensitive to such of its values to be highlighted and indifferent to others. CONFIRMA partners intend to respond to this challenge by taking into account, among other things, the contributions of social psychology combined with models of rhetoric. In this respect, the setting up of scenarios will help to emphasize the distinctions between counter-arguments according to the recipients.

• The analysis of the argument sbetween the different speeches of the same person and between several people within the different articles. Using argument mining techniques, we could better understand the exchanges between the different people cited in the same article as well as detect changes of position within the speeches of politicians or business leaders. Knowing, for example, who supports or defends such a point of view in a specific context, could make it easier for analysts using the service and participate in the construction of counter-arguments. Detecting the relationships between the arguments consists in deciding, given two fragments of text or conversation, whether the meaning of one of the fragments supports or attacks that of the other. The objective of this task is therefore to model the human capacity to know which relation exists between arguments proposed on the same subject. Different works have tried to put in place intelligent systems based on argumentation to allow debates between the system and the user, or between the users. Of course, it is thanks to this knowledge that the tool could propose counter-arguments.

MIREL- 690974 Page 25 of 36 14/10/2019

D5.2

Final exploitation plan

• The addition of the timeline for the dissemination of information could help to better understand who was at the origin of the fake news as well as the construction and animation of networks of actors who propagate fake news sites. An understanding of how a fake news spreads across different media will help optimize any response to counter-argument it. The focus is not only on the chronology between the different texts of the articles but also between the images of illustration that are used in order to detect the reuse of old images for misleading purpose.

• Development of new tools to counter-argument fake news. Current tools often present new information to readers contradicting the fake news. However, readers often generate counter-arguments refuting this new information (thus implicating their reliability of their source for example). The CONFIRMA project will develop interactive tools allowing readers to express the reasons for their rejection, and then give selected counter-arguments to answer those particular reasons.

• Data from experimental psychology will be used, as well as theoretical approaches to argumentation to understand what makes counter-arguments maximally effective. This research will be specifically applied to the case of counter-argumentation against fake news. Predictions will be tested using a set of empirical techniques, ranging from controlled experiments to focus groups.

3.9 WOODIe The project WOODIe (Whistleblowing open data impact: an implementation and impact assessment) has been retained for funding under the ISFP-2017-AG-CORRUPT18 call. The project is coordinated by the University of Torino (MIREL WP2 leader) and involves other five European partners.

This project focuses on whistleblowing legislation and open-data policy in public procurement. Whistleblower protection and open data are unanimously recognized as key measures to deter and detect corruption in public procurement. The project WOODIe will assess the current implementation and impact of these measures in seven Member States (Austria, Estonia, France, Ireland, Italy, Romania, and Slovenia) in order to develop an ICT tool for the public administrations (from local authorities to public companies). The long-term objective is to increase transparency and integrity in public procurement that contributes obtaining more efficient public expenditure, higher quality of goods, services and public work and more trust in government from the citizens.

Specifically, WOODIe will develop an impact assessment model on the basis of data collected from seven Member States (MSs). The model will be then operationalized in an 18 https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/isfp-2017-ag-corrupt

MIREL- 690974 Page 26 of 36 14/10/2019

D5.2

Final exploitation plan

ICT-tool for the public administrations to assess their whistleblowing/open data policy. Although the EU (as it would be clarified in the following lines), international institutions (OECD, Council of Europe) and NGOs at local and global level have recognized whistleblower protection and open data as key tools to address corruption and malpractices, there has been no assessment of the impact of these measures adopted by Member States or by EU Agencies and institutions. The WOODIe project wants to fill in this gap.

On the 3 October 2017, the European Commission Communication ‘Making Public Procurement work in and for Europe’19 has identified: (1) the availability of better and more accessible data, and (2) the establishment of an effective reporting mechanism able to protect whistleblowers (WBs) against retaliation as two main means to reach transparency and integrity in public procurement.

The complexity of the modern public or private organizations makes crucial to develop a climate of openness and integrity to address corruption. Open data and citizen engagement are a way to contribute to enhancing a culture of accountability and transparency; whistleblower protection is crucial to encourage the reporting of misconducts, frauds or corruption. EU is making several efforts to ensure data freely available for use and re-use, and this also includes public procurement. Open data, extensively investigated in MIREL, in particular with respect to the LegalRuleML standard20 (see, e.g., [Governatori et al., 2016], [Palmirani et al.2018]) are perceived as a tool to foster citizen participation and increase transparency of government.

In public procurement, in particular, the new Directives on public procurement have introduced e-Procurement as mandatory by 2018 also to allow for the integration of data-based approaches at various stages of the procurement process. Recently the H2020 Digiwhist project21 has promoted Opentender.eu, a tool that publishes tender data from 33 jurisdictions (28 Member States, Norway, The EU institutions, Iceland, Switzerland, Georgia). On a broader level, the Open Contracting Partnership has developed the Open Contract Data Standard (OCDS). Among the Member States analysed in the project, the French government commits itself to use this standard in its Open Government Action plan and is implementing the OCDS in a pilot in Bretagne. Also, Italy has since 2010 a National Database on Public Contracts (NDPC) developed by the Authority for the Supervision of Public Contracts (now Anti-Corruption Authority). EU is assessing the need, legal feasibility and scope for horizontal or further actions to strengthen the protection of whistleblowers. A public consultation has been carried out between March and May 2017 to collect views on whistleblower protection at EU and national level. Almost 6,000 people responded and showed strong support for the establishment of legally binding minimum standards on whistleblower protection in EU law. In July 2017, a study estimating the economic benefits 19 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2017%3A572%3AFIN 20 https://www.oasis-open.org/committees/legalruleml, [Athan et al, 2015] 21 http://digiwhist.eu/

MIREL- 690974 Page 27 of 36 14/10/2019

D5.2

Final exploitation plan

of whistleblower protection in public procurement carried out by Milieu Ltd. for the European Commission affirmed that the potential benefits of effective whistleblower protection for the EU in public procurement range between 5,8 to 9,6 billion each year. Some Member States (Romania and Ireland among the countries analysed in this project) have self-standing legislation; others have provisions to protect whistleblower in other laws (Austria, France, Italy, Estonia).

The project WOODIe intends to address the need for assessing the impact of whistleblower legislation and open data policy on local public procurement in order to improve the Member States and EU policies on the issue.

3.10 CO-CITY The project CO-CITY22, coordinated by University of Turin (MIREL WP2 leader) is the winner of the first Urban Innovation Action Call of EU23.

CO-CITY is intended to break the self reinforcing circle of poverty, social segregation in deprived neighborhoods and lack of participation. It achieves this by supporting the development of an innovative, polycentric “commons based urban welfare” composed of generative communities centre on urban commons, low cost service co-production, social mixing, and care of public spaces.

The city of Turin is coping with the consequences of a financial crisis that has contributed to the spread of poverty in old inner city neighbourhoods and peripheral areas. Between 2008 and 2013 the population of the city living in absolute poverty increased by 80%. 14.1% of the population live under the relative poverty line, while the grey area of people on the edge of poverty is enlarging. The unemployment rate is 13% and is rising more than in other Italian cities.

One main challenge to reducing poverty lays in how to break the circle of socio-spatial polarization: i.e. segregation, marginalization, and exclusion of citizens from citizenship and participation, both physically and socially.

CO-CITY is innovative in its legal, managerial and technological aspects, providing:

- an unconventional legal framework to enable citizens to take care of urban commons;

- an innovative ICT infrastructure for local social market and networking;

- management tutoring towards economic sustainability.

The authoritative approach is replaced by a collaborative one that considers citizens as potential changemakers, agents of virtuous circular processes of commoners’ welfare.

22 https://co-city.di.unito.it/ 23 https://uia-initiative.eu/en

MIREL- 690974 Page 28 of 36 14/10/2019

D5.2

Final exploitation plan

Meanwhile, the public sector evolves from being a service provider to being an enabler and a partner.

CO-CITY is supported by PP2-Unito's innovative FirstLife georeferenced social network (more interactive than Google Maps and not restricted to a circle of friends like Facebook), inspired by Ushahidi in Kenya. With FirstLife, citizens can share and discuss information, and create groups, via a map-based interface. FirstLife is multilingual and as such is also addressed to migrants.

Moreover, we will experiment with distributed ledger technology (at the basis of cryptocurrencies such as Bitcoin) not just to create a local currency, but also to create a more general exchange system where goods and skills are shared. While it is considered a disruptive technology in finance and e-government, in this project we apply it innovatively at the community level as a means of sustaining co-production and the core economy.

The platform developed in the project will then constitute a new use cases for the technologies explored in MIREL, in particular Big Data analysis and reasoning under legal constraints, investigated in MIREL WP3.

CO-CITY focuses on the cyclic process of “place production” rather than on a specific social target or deprived area. Empty public or private spaces become an opportunity instead of a cost and urban care becomes a collective task. This approach shares the logic of urban commons, helping to tackle social exclusion and neighbourhood deprivation, promoting commons’ use values rather than assets’ exchange values, and enabling hidden resources of social capital to emerge.

The legal tool will enable the underwriting of “pacts of collaboration” between citizens and the urban authority, under the new “Regulation on collaboration between citizen and the City for the care, shared management and regeneration of urban commons” approved in 2016 by City Council.

The technological infrastructure is aimed at recreating trust and the coordination of collective skills. We rethink the smart cities model from scratch, bringing people within any action via collaborative technologies. Management tutoring will help commoners activate sustainable local economies and build paths of autonomy to community led enterprises.

The toolkit will be the main reusable outcome of the project and the basis for its transferability. To get the process going CO-CITY will also provide:

- unused/underused buildings and open spaces;

- low-cost work (by direct initiative or by the provision of self-building equipment and ancillary job vouchers).

CO-CITY adopts a “revolutionary” conception of the role of public administration that encourages citizens’ commitment by defining a general framework of sharing responsibility

MIREL- 690974 Page 29 of 36 14/10/2019

D5.2

Final exploitation plan

and mutual trust. A group of active citizens (third sector or informal groups) identify a building or space by responding to a public call or presenting a proposal. Then they underwrite a “pact of collaboration” with the Urban Authority, defining the commoning programme, the respective powers, liabilities and expenses, insurance, etc.

3.11 CO3 A project similar to CO-CITY has been proposed by UNITO and retained for funding under the H2020-SC6-TRANSFORMATIONS-2018 call24.

This is the project CO3: Digital Disruptive Technologies to Co-create, Co-produce and Co-manage Open Public Services along with Citizens.

CO3 aims at assessing the benefits and risks of disruptive technologies, namely: blockchain, augmented reality, geolocated social network, liquid democracy tools and gamification, in the co-creation, co-production and co-management of public services with citizens as PAs partners.

In CO3, Augmented Reality (AR) becomes a single shared layer on the urban paysage and part of citizen’s public life, AR enables manipulation of financial objects built on blockchains, information sharing on a map, online deliberations and so constitutes an infrastructure for service co-production by citizens.

CO3 pilots the technologies’ ecosystem in three sites. It evaluates the outcomes of the new interaction model between PA and citizens under a set of metrics in three dimensions:

- social and cultural: citizen engagement, change in relationship with public servants;

- economic: value of services produced, effects on workplaces, consumptions and economic sustainability;

- legal: legal implications for PA including privacy and data protection. CO3 will devise a business plan ensuring long term sustainability for the PAs on the basis of the metrics applied on the pilots’ data.

The solutions investigated in MIREL will be of course exploited on the third (legal) dimension, in particular the Privacy Ontology (PrOnto) from the University of Bologna (MIREL WP1 leader) [Palmirani et al.2018] and the DAPRECO knowledge base [Robaldo and Sun, 2017], aiming at representing the provisions of the General Data Protection

24 https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/dt-transformations-02-2018-2019-2020;freeTextSearchKeyword=;typeCodes=0,1;statusCodes=31094501,31094502,31094503;programCode=null;programDivisionCode=null;focusAreaCode=null;crossCuttingPriorityCode=null;callCode=H2020-SC6-TRANSFORMATIONS-2018-2019-2020;sortQuery=openingDate;orderBy=asc;onlyTenders=false;topicListKey=topicSearchTablePageState

MIREL- 690974 Page 30 of 36 14/10/2019

D5.2

Final exploitation plan

Regulation in a machine-readable format built on top of past research results at the University of Luxembourg (MIREL coordinator).

Specifically, the CO3 project will build an analytical framework for studying the potential benefits and risks of new disruptive technologies such as Blockchain, Augmented Reality, Geolocation in Social Networking, Opinion Formation and Gamification and their potential to foster co-creation, co-production and co-management of open public services.

The project aims to properly evaluate their social impact, identify emerging best practices, understand how to overcome legal barriers, individuate metrics for a long-term sustainability plan and define appropriate applications.

Blockchain is the key enabling technology for CO3’s thesis: it represents a revolution in how information is gathered and collected. By creating inter-organizational databases, the Blockchains or, more in general, distributed ledger technologies allow a paradigm shift at all levels. Blockchains can be applied to homogeneous entities, for example to consortia of financial institutions, enterprise alliances or can swiftly connect different branches of Public Administrations. However, less is known about the possible consequences of a general interconnection of parties different by nature, for example a Public Administration could be connected with a system of enterprises jointly managing some kind of operations and creating a new form of public-citizens partnership. The nature of Blockchain technology has got imaginations running wild, because the idea can now be applied to any need for a trustworthy record: CO3 wants to test the hypothesis that Blockchains could lead to a generalized cooperation system between individuals, groups and public authorities. And such framework is very similar to the aforementioned commoning model. Actually, a Blockchain is a common good in itself, a distributed infrastructure: not by chance, the pseudonymous creator of bitcoin announced the invention of the Blockchain for the first time on the Peer to Peer Foundation’s web forum - an organization advocating a commons-based peer production model. On the one hand, it is not easy to fully participate in and benefit from this innovation. Coordinating between peers with the aid of Blockchain-based public ledgers requires time, improved ICT competences and significant mastery of cryptocurrencies.

On the other hand, Augmented Reality is a high potential technology whose implications are far from being understood. In fact its applications presently regard a relatively tiny market, largely occupied by a single game (Pokemon go) and frivolous marketing operations.

The EU project SONNETS25 found utility in AR's for the Public Administrations only in educational sector; however, in our analysis, AR is the ideal interaction layer for Blockchains, and can be conducive to a wider acceptance and to a large class of value bearing innovations in local economies.

25 https://www.sonnets-project.eu/

MIREL- 690974 Page 31 of 36 14/10/2019

D5.2

Final exploitation plan

CO3 will address AR in a particular interpretation, almost untapped by the market: AR can be studied in an urbanistic perspective, and so should receive the highest attention by PAs. In fact, such technology can construct a single shared layer which is part of the paysage, therefore carrying ecological problems. AR can be part of citizen’s public life and become a core interface for the Municipalities embracing smart city programs. A disruptive technology cannot be assessed alone, the future will be the result of the convergence of several innovations that blossom on the trend towards a network society: CO3 will study the potential of Blockchain, AR, Geolocation in Social Networks, Opinion Formation tools and Gamification under the unifying lens of the commoning model.

3.12 SANKOFA The partner and work package leader UNIBO, also thanks to the experience gained during the MIREL secondments at Stanford University, applied and won the United Nation challenge "Automatic Information Extraction and Knowledge Elicitation"26, with the project SANKOFA - Semantic Annotation of Knowledge Featuring Akoma Ntoso.

The SANKOFA project intends to produce a web applications (API) that is capable to mark-up the UN resolutions using Akoma Ntoso XML vocabulary [Palmirani2011] [Palmirani and Vitali2011], to qualify the paragraphs according to the role played into the structure (e.g., preambular or operative) and finally to classify the semantic parts of the provision (e.g. references). The project intends also to qualify some peculiar textual parts (e.g. person, organization, date, quantity, action, and event). The project will use a hybrid solution using NLP techniques for detecting structure, references, presentational parts, annexes, inline elements (e.g. symbol, committee, session), etc., by employing parsers, vocabularies, NER, regular expressions, patterns, frames, deep learning for detecting the knowledge in the text, and to represent it with semantic annotation.

After the qualifications of the textual structures and the semantics of relevant segments, the SANKOFA framework intends to associate the correct conceptual classes from the given ontologies (e.g., UNDO, SDGIO, UNBIS) with the appropriate text segments, and to create assertions and relationships (e.g. which decision was taken, the timing of the decision, which actors were involved, which results are expected). FRBR and ALLOT Top Level Class are pillars in this solution. Those assertions will be also serialized in RDF following the Linked Open Data principles.

More specifically, SANKOFA framework will achieve the aforementioned objective the through the following steps:

26 http://unite.un.org/news/university-bologna-team-wins-first-prize-united-nations-general-assembly-resolutions-extraction

MIREL- 690974 Page 32 of 36 14/10/2019

D5.2

Final exploitation plan

1. Detect NER like role, event, organization, location, date.

2. Classify the sentences that talk about SDG using the definitions of the SDGIO.

3. Qualify the sentences in operational and preambulary using linguistic patterns and also to detect the action proposed (e.g., noting, decides).

4. Recognise the document structure in the main parts: coverPage, preface, preamble, mainBody, conclusions, annexes. We use also the information detected in the first three steps in order to distinguish the hierarchical structure of the document and to qualify the preambular sentencens from the operational ones.

5. Convert all the extracted knowledge in Akoma Ntoso.

6. Interpret the extracted knowledge and to create semantic assertion using to the existing ontologies (e.g., ALLOT, UNDO, SDGIO, etc.). The project aims at creating a RDF repository with those assertions.

Akoma Ntoso provides tags27 to annotate, either manually or via entity-linking and concept-mining NLP techniques, textual information with respect to predefined classes and individuals belonging to legal (computational) ontologies, which may be stored into repositories of RDF triples.

Thanks to the use of Akoma Ntoso and legal ontologies, which were widely studied and employed by MIREL partners in their research activities (see [Casini et al.2015], [Bartolini et al.2016], [Ajani et al.2017], [Palmirani et al.2018], among others), SANKOFA will:

1. Produce an Akoma Ntoso technically valid but also semantically sound following the OASIS LegalDocML specifications level 2, sublevel D28;

2. Use authentic sources and authoritative information, using FRBR approach for declaring the provenance;

3. Design the solution following ontology design patterns principles;

4. Provide a scalable method that is customizable also for other UN agencies;

5. Design a modularized solution that is adaptable to other kind of documents (e.g., report of conference, order of the day, constitution, basic texts, etc.);

6. Apply the same tools, with a minimal customization, for the other five languages of UN using the principle of portability and customization.

As it is well-know, computational ontologies encode formal naming and definitions of the concepts involved in the modeled domain, thus enabling reuse and cross-document

27 http://www.akomantoso.org 28 Described in paragraph 7 at http://docs.oasis-open.org/legaldocml/akn-core/v1.0/os/part1-vocabulary/akn-core-v1.0-os-part1-vocabulary.html#_Toc523925096

MIREL- 690974 Page 33 of 36 14/10/2019

D5.2

Final exploitation plan

navigation and search. The ontological concepts may also be organized and connected to each other via basic semantic relations (is-a, part-of, etc.) that enable basic forms of reasoning, and they can be linked to other concepts from external public ontologies from the Web of Things, including Linked Open Data (LOD), thus enhancing the interoperability, the standardization, and the reasoning capabilities of the resources.

3.13 Zhejiang University - University of Luxembourg Joint Laboratory on AIs, Robotics and Reasoning (ZLAIRE)

In 2018-2019, University of Luxembourg and Zhejiang University strengthen their links by running several research activities on topics related to the theoretical foundations of MIREL, specifically argumentation and deontic logic (see [Baroni et al., 2018], [Gabbay et al., 2018], [Liao et al., 2016a], [Liao et al., 2016b], [Liao et al., 2017], [Liao et al., 2019], [Liao and van der Torre, 2017], [Liao and van der Torre, 2018], [Malerba et al., 2017]). Prof. Leon van der Torre, PI of MIREL, and prof. Beishui Liao, respectively visited Zhejiang University and University of Luxembourg in MIREL in several occasions. Other researchers from the two universities visited as well the other university for short periods.

The long-standing research collaboration between prof. van der Torre and prof. Liao indeed started before MIREL, in particular in the context of the INTER mobility project “When computers start understanding misunderstandings”29, supported by the Fond National per la Recherche (FNR), the main funding research institution of Luxembourg.

In light of the two projects, prof. van der Torre and prof. Liao, together with the researchers in their teams, will continue the collaboration in the future, while framing it within a new framework agreement called “Zhejiang University - University of Luxembourg Joint Laboratory on AIs, Robotics and Reasoning” (or briefly, ZLAIRE)30.

The scope of ZLAIRE, currently under definition at the administrations of the two universities, is broader than MIREL’s, which is instead restricted to Legal Informatics.

The general research goal of ZLAIRE is to investigate and shape human-AI interaction. This encompasses modelling and studying high-level cognitive tasks of intelligent agents, like reasoning, learning, inquiry, planning, decision-making, communication and argumentation, and their relation to lower-level tasks like perception, action, and basic language processing.

The major research directions of ZLAIRE are as follows:

• Formal Cognition: Working on a general theory of intelligent systems in real-world environments, with a focus on formally modelling and integrating qualitative and

29 https://www.fnr.lu/research-with-impact-fnr-highlight/when-computers-start-understanding-misunderstandings/ 30 https://zlaire.uni.lu

MIREL- 690974 Page 34 of 36 14/10/2019

D5.2

Final exploitation plan

quantitative accounts; investigating the possibilities and limitations of artificial vs human intelligence, exploring the theoretical foundations of the epistemology and ethics of intelligent beings;

• Data, Knowledge, and Decisions: From data to reasoning and decision-making: studying how to extract and represent knowledge from - big or small - data, how to reason on formalized (machine-readable) knowledge, and how to use reasoning for decision making;

• Humans and Robots: Studying how artificial intelligent agents may perceive and understand humans’ requirements, how humans may perceive and understand the actions and decisions of artificial agents, how agents and humans may jointly perceive and understand the world, and how humans and agents may interact to achieve common goals;

• Applications: logistics, social robotics, autonomous exploration systems, etc.

Some academic initiatives are already planned in ZLAIRE for the year 2020, notably:

• Prof. Leon van der Torre was appointed as guest professor of Zhejiang University. The “FNR Inter Mobility” programme has thus made international mobility and networking sustainably possible. Moreover, prof. van der Torre has been recruited as national high-end foreign expert in the “High-end Foreign Experts Recruitment Plan”31 2019 call. This is a new programme launched for the first time in 2019 by the Chinese Ministry of Science and Technology (MOST) 32 , with the objective of introducing in China a batch of high-level foreign experts in key priority fields, so to contribute to the country’s sci-tech innovation development. Mehdi and Leon will work with Beishui on non-monotonic reasoning based on argumentation. New challenges are emerging on the use of argumentation for explainable AI, machine ethics, multi-agent systems, and decision making.

• In April 6-9 2020, University of Luxembourg and Zhejiang University will jointly organize the Zhejiang Logic for AI summit (ZjuLogAI 2020) 33 at Zhejiang University. With its special focus theme on Explainable AI, the summit intends to promote the interplay between logical approaches and machine learning based approaches in order to make AI more transparent and accountable. Four sub-events will be organized in the summit by researchers at the two universities: (1) the 5th Asian Workshop on Philosophical Logic (AWPL), (2) the 3rd International Conference on Logic and Argumentation (CLAR 2020), (3) the 6th Global Conference on Artificial Intelligence (GCAI 2020), and (4) the AI and Art exhibition. The summit is the second edition of the the Luxembourg Logic for AI Summit

31 http://chinainnovationfunding.eu/project/2019-high-end-foreign-experts-recruitment-plan 32 http://www.most.gov.cn/eng 33 http://www.xixilogic.org/zjulogai/

MIREL- 690974 Page 35 of 36 14/10/2019

D5.2

Final exploitation plan

(LuxLogAI), organized in Luxembourg in September 201834.

References [1] [Ajani et al, 2017] G. Ajani, G. Boella, L. Di Caro, L. Robaldo, L. Humphreys, S. Praduroux,

P. Rossi, A. Violato: The European legal taxonomy syllabus: A multi-lingual, multi-level ontology framework to untangle the web of European legal terminology , Applied Ontology, to appear.

[2] [Antoniou et al, 2018] Antoniou, G., Baryannis, G., Batsakis, S., Governatori, G., Robaldo, L., Siragusa, G. and Tachmazidis, I. Legal Reasoning and Big Data: Opportunities and Challenges., in proc. of the 3rd Mining and Reasoning on Legal text (MIREL2018) workshop

[3] [Athan et al, 2015] Tara Athan, Guido Governatori, Monica Palmirani, Adrian Paschke, Adam Z. Wyner: LegalRuleML: Design Principles and Foundations. Reasoning Web 2015.

[4] [Baroni et al., 2018] Pietro Baroni, Massimiliano Giacomin, Beishui Liao. A general semi-structured formalism for computational argumentation: Definition, properties, and examples of application, In Artificial Intelligence, volume 257, 2018.

[5] [Bartolini et al.2016] Bartolini, Cesare, Andra Giurgiu, Gabriele Lenzini, and Livio Robaldo. 2016. Towards legal compliance by correlating standards and laws with a semi-automated methodology. In BNCAI, volume 765 of Communications in Computer and Information Science, pages 47{62. Springer.

[6] [Boella et al, 2016] G. Boella, L. Di Caro, L. Humphreys, L. Robaldo, P. Rossi, L. van der Torre: Eunomos, a legal document and knowledge management system for the Web to provide relevant, reliable and up-to-date information on the law, Artificial Intelligence and Law, Vol. 24, Issue 3.

[7] [Bowman et al., 2015] Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015. A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326.

[8] [Casini et al.2015] Casini, G., T. Meyer, K. Moodley, U. Sattler, and I. Varzinczak. 2015. Introducing defeasibility into owl ontologies. In Robert Meersman, Pilar Herrero, and Tharam Dillon, editors, Proc. of International Semantic Web Conference (ISWC).

[9] [Gabbay et al., 2018] Present and Future of Formal Argumentation. Dov M. Gabbay, Massimiliano Giacomin, Beishui Liao, Leendert W. N. van der TorreDagstuhl Perspectives Workshop, In Dagstuhl Manifestos, volume 7, 2018.

[10] [Governatori et al., 2016] Guido Governatori, Mustafa Hashmi, Ho-Pun Lam, Serena Villata, Monica Palmirani: Semantic Business Process Regulatory Compliance Checking Using LegalRuleML. EKAW 2016: 746-761

[11] [Liao et al., 2016a] Beishui Liao, Nir Oren, Leendert van der Torre, Serena Villata. Prioritized 34 https://luxlogai.uni.lu

MIREL- 690974 Page 36 of 36 14/10/2019

D5.2

Final exploitation plan

Norms and Defaults in Formal Argumentation, In Proceedings of the 13th International Conference on Deontic Logic and Normative Systems (DEON2016), 2016.

[12] [Liao et al., 2016b] Kang Xu, Beishui Liao, Zhe Yu. An Empirical Comparison of Argumentation Formalisms (Kang Xu Beishui Liao Zhe Yu), In Proceedings of the Workshop on 'MIning and REasoning with Legal texts' collocated at the 29th International Conference on Legal Knowledge and Information Systems. 2016.

[13] [Liao et al., 2017] B. Liao A. Malerba A. Tettamanzi L. van der Torre A. Rotolo, C. Da Costa Pereira. - combining fuzzy logic and formal argumentation for le-gal interpretation. In Proc. of the 26th International Joint Conference on Artificial Intelligence (IJCAI2017), 2017.

[14] [Liao et al., 2019] Beishui Liao, Nir Oren, Leender van der Torre, and Serena Villata. Pri-oritized norms in formal argumentation. Journal of Logic and Computation, 29(2), 2019.

[15] [Liao and van der Torre, 2017] B. Liao and L. van der Torre. - defense semantics of argumentation: encod-ing reasons for accepting arguments. In The paper has been presented on MIREL workshop at ICAIL. It will be resubmitted to AICOL series., 2017.

[16] [Liao and van der Torre, 2018] Beishui Liao and Leendert W. N. van der Torre. Representation equiva-lences among argumentation frameworks. In Computational Models of Ar-gument - Proceedings of COMMA 2018, Warsaw, Poland, 12-14 September 2018, pages 21–28, 2018.

[17] [Malerba et al., 2017] A. Malerba A. Rotolo A. Tettamanzi L. van der Torre S. Villata C. da Costa Pereira, B. Liao. - handling norms in multi-agent systems by means of formal argumentation. In IfCoLog Journal of Logics and their Applications, Volume 4 (9)., 2017.

[18] [Palmirani2011] Palmirani, Monica, 2011. Legislative Change Management with Akoma-Ntoso, pages 101{130. Springer Netherlands, Dordrecht.

[19] [Palmirani and Vitali2011] Palmirani, Monica and Fabio Vitali, 2011. Akoma Ntoso for Legal Documents, pages 75{100. Springer Netherlands, Dordrecht.

[20] [Palmirani et al.2018] Palmirani, Monica, Michele Martoni, Arianna Rossi, Cesare Bartolini, and Livio Robaldo. 2018a. Legal ontology for modelling GDPR concepts and norms. In Legal Knowledge and Information Systems - JURIX 2018: The Thirty-first Annual Conference, Groningen, The Netherlands, 12-14 December 2018.

[21] [Robaldo and Sun, 2017] L. Robaldo and X., Sun: Reified Input/Output logic: Combining Input/Output logic and Reification to represent norms coming from existing legislation, The Journal of Logic and Computation, Vol. 27, Issue 8.