José Saenz
email: jose.saenz@iff.fraunhofer.de

Jule Bessler-Etten

Marcello Valori
email: marcello.valori@stiima.cnr.it

Gerdienke B Prange-Lasonder

Irene Fassi
email: irene.fassi@stiima.cnr.it

C Bidard

A B Lassen

I Paniti

A Toth

T Stuke

S Wrede

K Nielsen

An online Toolkit for applications featuring collaborative robots across different domains

Keywords: human-robot interaction, intelligent robots, occupational safety, robots, safety, standardization I. I

Collaborative robots (cobots) are being applied in areas such as healthcare, rehabilitation, agriculture and logistics, beyond the typical manufacturing setting. This is leading to a marked increase in the number of cobot stakeholders with little or no experience in traditional safety engineering. Considering the importance of human safety in collaborative robotic applications, this is currently proving to be a barrier to more widespread cobot usage. A web-based Toolkit that targets cobot end-users and manufacturers with varying levels of safety expertise was developed, helping them to understand how to consider the safety of their cobot applications. In this work we will provide an overview of the state of the art for ensuring cobot safety, highlight the support provided by the "COVR Toolkit" and introduce three examples where third parties applied the Toolkit for their collaborative robotics application.

concept beyond the industrial domain. Another current trend in robotics is the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for typical challenges such as perception in unstructured environments, behavioral adaptation, and improved interaction with humans. Taken together, there is an overall increase in the number of robotics stakeholders working on collaborative applications and who have little or no experience in safety engineering.

The flexibility of current robotics technologies means that the same robotic hardware can be used for a variety of applications, e.g. both in the industrial or in the healthcare domains. This "cross-domain" nature of robotics is a boon to robot manufacturers and users alike, but presents special challenges from the perspective of safety and standardization. International standards represent the state of the art and best practices. In the European Union, the application of harmonized standards, while voluntary, confer upon users the assumption of conformity to essential health and safety requirements. In an increasingly uncertain world, such assumption of conformity is a helpful way for manufacturers to ensure that their system is safe. Standards are currently organized according to specific domains. As an example, there are separate standards for industrial robots used in manufacturing and for robots used for healthcare and rehabilitation activities. Although it is now possible to use the same robotic hardware for different domains, they can be subject to different and not entirely synchronized requirements, based on the domain the robot is being used (e.g. using an exoskeleton in the factory or for rehabilitation purposes). This creates uncertainty for manufacturers and endusers alike.

Previous work [START_REF] Saenz | COVR -towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains using robot safety skills[END_REF][5] has addressed the challenge arising THMS-0386 due to the cross-domain nature of cobots through the introduction of the concept of safety skills. This term has recently been refined -and referred to as "safety-relevant human-robot collaboration skills, HRC skills" -in the development of a CEN Workshop Agreement [START_REF]Guidelines for the development and use of safety testing procedures in human-robot collaboration[END_REF]. It is defined as an abstract representation of the ability of an HRC application to reduce a risk, irrespective of the implementation. It builds on the idea that mechanical hazards can be similar and independent of the domain of the application, and is supported to a certain extent by current cross-references between standards from different domains (e.g. the ISO/TR 23482-1 [START_REF]Application of ISO 13482-Part 1: Safety-Related Test Methods; International Organization for Standardization[END_REF], which relates to service robots, references the ISO/TS 15066 [START_REF]Robots and Robotic Devices-Collaborative Robots; International Organization for Standardization[END_REF], which is specific for industrial robots). This approach is particularly useful when considering how to execute a system level validation measurement (SLVM) of an HRC application.

The how-to guides for executing SLVM are known as system level validation protocols, hereafter referred to as "protocols".

The main contribution of this paper is to describe the COVR Toolkit that supports various cobot stakeholders in understanding and validating the safety of their application. The paper provides examples from three COVR Awards on how they used the Toolkit and reports on feedback for improvements that were suggested by Toolkit users. This paper is structured as follows. We begin with an overview on the state of the art for ensuring safety of HRC applications and a description of a specific lifecycle model for the design and implementation of HRC applications as the motivation for the Toolkit. This will be followed by a description of the Toolkit as a freely available website to support robotics stakeholders across all their life cycle activities. Then we introduce three exemplary COVR Award projects, highlight their use of the Toolkit and feedback they provided. The paper will end with an outlook on future developments of the Toolkit including COVR Hubs as a means for community-building beyond the end of the project.

II. STATE OF THE ART

A. Essential health and safety requirements for human-robot interaction

Within the European Union, the EC Machinery Directive MD/2006/42 specifies the essential health and safety requirements for all machines (including collaborative robots for all domains such as manufacturing, healthcare or rehabilitation) introduced to the market. This directive also explains the requirements for achieving self-conformity (via the CE mark [9]) and references harmonized standards for further guidance about the essential health and safety. While conformity to these harmonized standards is not compulsory, their application does offer manufacturers a high degree of certainty that their systems are safe and in the event of an accident, puts the burden of proof or negligence misdeed on the regulatory body and not the manufacturer. Given this situation, robotics system integrators and manufacturers often choose this path and conform with harmonized standards.

Additionally, the EU regulation 2017/745 (European Parliament and Council of the European Union, 2017), also known as the Medical Device Regulation (MDR) [START_REF]Regulation (EU) 2017/745 on Medical Devices[END_REF] needs to be observed by robots used in healthcare and rehabilitation. The MDR emphasizes safety and performance across the lifetime of the application.

The challenges arising due to the transversal nature of robots with respect to domains have been discussed in [START_REF] Bessler | Safety Assessment of Rehabilitation Robots: A Review Identifying Safety Skills and Current Knowledge Gaps[END_REF]. In particular, it is not always clear for manufacturers or end-users which standards are applicable to their systems or how to deal with conflicting recommendations from different standards.

Previous research has addressed the challenge of considering safety in HRC applications through the use of model-based approaches, either in combination with automated techniques for identifying risks and considering erroneous human behavior [START_REF] Askarpour | Formally-based Model-Driven Development of Collaborative Robotic Applications[END_REF] or in combination with traditional faulttree analysis [START_REF] Yakymets | Model-Driven Safety Assessment of Robotic Systems[END_REF]. An extensive list of guidelines for prevention of mechanical hazards for HRC applications in the industrial domain are developed in [START_REF] Gualtieri | Development and Validation of Guidelines for Safety in Human-Robot Collaborative Assembly Systems[END_REF]. Such approaches have proven to be useful in reducing the overall number of mechanical hazards in specific applications, but are still limited to the industrial domain.

In the healthcare domain, researchers have used musculoskeletal modeling [START_REF] Zhou | Lower limb exoskeleton parasitic force modeling and minimizing with an adaptive trajectory controller[END_REF] and numerical simulations [START_REF] Zhou | A human-centered design optimization approach for robotic exoskeletons through biomechanical simulation[END_REF] as a means to identify loads on joints and other structures to assess the effects of specific device designs [START_REF] Agarwal | Simulation-Based Design of Exoskeletons Using Musculoskeletal Analysis[END_REF] and assess risks associated with incorrect alignment or an incorrectly implemented limitations to the ranges of motion [START_REF] Tröster | Simulation Framework for Active Upper Limb Exoskeleton Design Optimization Based on Musculoskeletal Modeling Simulation Framework for Active Upper Limb Exoskeleton Design Optimization Based on Musculoskeletal Modeling[END_REF] [START_REF] Grabke | Lower Limb Assistive Device Design Optimization Using Musculoskeletal Modeling: A Review[END_REF]. These simulation-based approaches all suffer from uncertainties regarding the validity of model behavior, due to underlying assumptions regarding, for example, human joint mechanics or tissue characteristics.

The agricultural domain is affected by the increasing introduction of robotics and automation, aimed at increasing productivity through automated execution of operations such as harvesting, weeding, and pruning [START_REF]Agricultural robotics: the future of robotic agriculture[END_REF]. However, the so-called "Agriculture 4.0" revolution is still far from its full implementation [START_REF] Zambon | Revolution 4.0: Industry vs. agriculture in a future development for SMEs[END_REF]. Nevertheless, agricultural robotics is widely addressed by the scientific literature of the last decades, with human-robot collaboration representing one emerging topic: in [START_REF] Benos | Safety and ergonomics in human-robot interactive agricultural operations[END_REF], HRC safety and ergonomics issues are extensively discussed from the perspective of the agricultural domain; in [START_REF] Cheein | Human-robot interaction in precision agriculture: sharing the workspace with service units[END_REF], issues deriving from farm workspace sharing are analyzed, proposing guidelines for HRC strategies; [START_REF] Lytridis | An overview of cooperative robotics in agriculture[END_REF] reports a detailed review of cooperative robotics in agriculture including a comprehensive summary of HRC-related studies.

The authors believe that one way to support the robotics community in evaluating the safety of HRC applications is to provide detailed instructions on how to measure the efficacy of implemented safety measures. This is a trend increasingly seen in recent standardization activities. As an example, the recent final draft international standard (FDIS) version of the ISO/FDIS 10218-2 [START_REF] Lytridis | An overview of cooperative robotics in agriculture[END_REF], published in 2021 and currently in evaluation, has added information to guide users through the process of executing a validation measurement for when the operating mode power and force limiting (PFL) is used. Other standards which also define testing procedures include the ISO/TR 23482-1, the ISO 3691-4 [START_REF]Industrial Trucks-Safety Requirements and Verification-Part 4: Driverless Industrial Trucks and Their Systems; International Organization for Standardization[END_REF], and the ISO 18646 series. The ISO/TR 23482-1 specifies test methods to verify compliance to the requirements of 13482, and is applicable to THMS-0386 personal care robots. The ISO 3691-4 is intended to be used for driverless industrial trucks and automatically guided vehicles. It defines procedures to test the stability of the vehicle, as well as for human detection. Guidance for measuring rated speeds and stopping characteristics is provided by the ISO 18646-1 [START_REF]Robotics-Performance Criteria and Related Test Methods for Service Robots-Part 1: Locomotion for Wheeled Robots; International Organization for Standardization[END_REF]. Additionally, the ISO 18646-2 [START_REF]Robotics-Performance Criteria and Related Test Methods for Service Robots-Part 2: Navigation; International Organization for Standardization[END_REF] specifies methods to evaluate obstacle detection and obstacle avoidance functionalities. Navigating which standards apply for which applications can be challenging for novice robotics stakeholders, especially in situations where the same robotic hardware is used in multiple domains. Concerning medical robotics, specific standards have to be considered, even concerning the risk assessment: while ISO 12100 [START_REF]Safety of Machinery-General Principles for Design-Risk Assessment and Risk Reduction; International Or-ganization for Standardization[END_REF] is the Type A reference standard for the safety of machinery and describes the related risk assessment process, ISO 14971 [START_REF]Medical devices -Application of risk management to medical devices[END_REF] introduces the concept of benefit-risk analysis and provides instructions for the risk management process, which are both crucial for all the medical products. The standards from the IEC 60601 series address the different categories of medical electrical equipment. IEC 60601-1 [START_REF]Medical electrical equipment -Part 1: General Requirements for basic safety and essential performance[END_REF] reports the main safety and performance requirements, which are complemented and, eventually, replaced, by more specific standards of the series. Particularly for robotic devices, IEC 80601-2-77 [31] addresses robotically assisted surgical equipment (RASE), while IEC 80601-2-78 [START_REF]Medical electrical equipment -Part 2-78: Particular requirements for basic safety and essential performance of medical robots for rehabilitation[END_REF] focusses on medical robots for rehabilitation, assessment, compensation or alleviation.

B. Life cycle model for HRC applications

For the development and deployment of HRC applications, six particular life cycle phases can be identified. Their relation to the generic life cycle model is shown in Fig. 1. This six-phase model served as the basis for the development of the COVR Toolkit, which aims to be the most comprehensive resource for supporting robot stakeholders in ensuring the safety of their HRC applications. In the following, we will describe these six phases in detail, highlighting stakeholder needs.

Fig. 1. Generic life cycle model (gray) and corresponding life cycle phases for cobot application development as defined by COVR stakeholders

The first life cycle phase pertains to users seeking inspiration from available solutions, to identify the state of the art. In particular, stakeholders are interested in learning about the safety challenges and how they were handled. We expect the level of background knowledge to vary across stakeholders, so any offering should be simple enough for novices to understand, while also offering enough details that are of relevance for more expert users.

During the second life cycle phase "Requirements specifications", stakeholders are interested in identifying which standards, regulations, or laws are applicable to their system. As in phase 1, we expect stakeholders to have varying levels of expertise and background knowledge, whereby here it would be more important to cater to those with a lower level of experience.

A typical task during the third life cycle phase, "Design" is the execution of a risk analysis. This is often carried out in correspondence to ISO 12100 [START_REF]Safety of Machinery-General Principles for Design-Risk Assessment and Risk Reduction; International Or-ganization for Standardization[END_REF]and includes the sub-tasks of identification of hazards, calculation of risk, and identification of risk reduction measures (RRM). Although the execution of risk analyses is common within industry, novel HRC applications feature additional hazards that can be confusing. We expect that stakeholders interested in support for execution for a risk analysis to have a wide variety of expertise, whereby a focus on novice users is preferable.

Phase 4 "Implementation" spans all activities prior to normal operation, such as commissioning and software testing. In the following phase, "Verification/validation", a validation measurement is typically executed. This serves as proof that the implemented RRM are sufficient to meet safety requirements. Therefore, this phase should address the how-to aspect of such validation measurements.

During Phase 6 "Operation", it is important to observe the system performance and adapt the risk assessment if necessary. Additionally, any change to the system also needs to be reviewed, to identify whether the risk analysis needs updating or if new validation measurements are necessary.

The Toolkit was developed to systematically address the safety related needs of stakeholders in all of the life cycle phases for the design and implementation of HRI applications. Further details on specific engineering tasks involved in each phase have been reported in [START_REF] Saenz | COVR Toolkit -Supporting safety of interactive robotics applications[END_REF]. To the best of the authors' knowledge, there are no comparable online services or guides that systematically support coboteers along all these life cycle phases.

C. Cross-domain safety skills approach

As previously mentioned, six HRC safety skills were identified through a top-down and bottom-up approach [START_REF] Saenz | COVR -towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains using robot safety skills[END_REF]. The top-down method analyzed existing standards to identify operating modes or methods for safeguarding HRC applications. The bottom-up approach defined a large range of HRC applications from a variety of domains and then identified possible risk mitigation methods for typical hazards. HRC safety skills are an abstract representation of the ability of an application to reduce a risk defined irrespective of the implementation and can be validated for specific applications at a system level. These skills are: maintain safe distance (MSD), maintain dynamic stability (DYS), limit physical interaction energy (LIE), limit range of movement (LRM), maintain proper alignment (MPA), and limit restraining energy (LRE). It is important to underline that these HRC safety skills are not meant to replace existing definitions or supersede available standards. They were designed to simplify system level safety validation and allow for the development of protocols that are THMS-0386 transversal with respect to robot application domains. An overview of the relationship between operating modes from existing standards and the six safety skills is reported in [START_REF] Valori | Validating Safety in Human-Robot Collaboration: Standards and New Perspectives[END_REF].

III. THE COVR TOOLKIT AS A KNOWLEDGE BASE

The COVR Toolkit (www.safearoundrobots.com) is a webaccessible knowledge base (Fig. 2) for cobot stakeholders interested in safety of HRC applications [START_REF] Saenz | COVR Toolkit -Supporting safety of interactive robotics applications[END_REF]. The first version consisted of a simple database to identify applicable standards and regulations based on the robot type and application domain of an HRC application. It also featured a database of system level validation methods, which could be filtered according to their domain, safety skill involved, and type of robotic device.

Fig. 2. Directives & Standards finder on the COVR Toolkit

From 2019-2021, the Toolkit was updated in yearly cycles to systematically address the needs of several stakeholders along the different life cycle phases. In the following, the various aspects of the Toolkit and their relation to the lifecycle phases from the previous section will be described.

A. Phase 1 support -Case Stories

Case stories in the Toolkit are meant to support coboteers during Phase 1 "Inspiration and technology scan" of the lifecycle for cobot development. The Toolkit currently features over 50 case stories. These downloadable pdf documents compactly describe challenges, solutions and lessons learned from the projects funded by the COVR consortium (COVR Awards). In order to create value for the target community, these case stories feature a high level of transparency regarding safety challenges and considerations. Although case stories are intentionally kept brief, they often link to other documents such as detailed project deliverables with experimental data or risk assessments. Case stories are intended to be understandable by people with a low level of HRC safety background knowledge, whereas the additional documents are targeted towards stakeholders with higher levels of expertise.

B. Phase 2 support -Directives and Standards

To support robotics stakeholders during Phase 2 activities, the Toolkit features the subsection "Directives and Standards". It works as a filter to identify directives and standards relevant for a specific combination of operating domain and type of robot components. The filter currently differentiates between combinations of six different types of cobot devices. The filters match the level of detail needed to differentiate between different directives and standards, and are intended to be nonetheless simple and understandable for novice robotics stakeholders.

C. Phase 3 and 4 support -Risk Assessment

It is necessary to conduct a risk assessment for all HRC applications, and there was genuine interest, especially among novice robotics stakeholders, in practical support with this activity. This work corresponds to Phase 3, and in the case of changes to an operating system, Phase 4 of the life cycle model. The Risk Assessment sub-section summarizes the individual steps for executing a risk assessment. It features a number of short explanatory videos describing typical hazards and mentioning possible corresponding risk mitigation methods.

While many companies have safety experts who are well versed in the execution of a risk assessment for typical machines, they often have less experience with HRC applications. This was the rationale behind the practical examples of typical hazards, as well as a downloadable risk assessment template. This sub-section was also designed for stakeholders with varying levels of background knowledge. Expert stakeholders will appreciate best practice examples, while non-experts are targeted through the videos and step-bystep explanations.

D. Phase 5 support -Protocols

Over thirty different step-by-step guides, called protocols, were specifically developed to explain how to execute SLVM for HRC applications based on the types of robot device and the applied HRC safety skill. The sub-section Protocols features a filtering tool similar to the Directives & Standards sub-section, to help users to find applicable protocols. In the case of protocols, there is no filter for domain (e.g. healthcare, manufacturing, or logistics), in contrast to the Directives & Standards sub-section, since protocols are meant to be valid across all domains. Any application-specific attributes that should be considered when executing an SLVM (e.g. specification of certain situations/environments) are identified within the protocols themselves. Protocols can be downloaded and used when planning and executing SLVM during Phase 5 of the life cycle model. To provide additional support to novice stakeholders, a number of online videos have been created and linked to the corresponding protocol.

E. General background information -Test Equipment, Publications, Legal Tool and FAQs

In addition to the Toolkit sub-sections that correspond directly to the life cycle model, there are four further features that provide useful background information for stakeholders. The sub-section "Test Equipment" contains a database of measurement equipment that is suitable for the execution of SLVM and is closely related to the protocols. Whereas protocols are by design measurement equipment agnostic, users have indicated that they would also appreciate support in finding suitable measuring equipment. The database can be THMS-0386 filtered according to the specific physical dimension to be measured, such as pressure, distance, etc. The library is the result of market research, but should not be considered as an endorsement for any specific manufacturer. The sub-section also solicits Toolkit stakeholders to suggest new devices to be included in the database.

The "Publications" sub-section of the Toolkit lists over 90 different publications, that are relevant to cobot safety. The publications were initially curated by the COVR consortium based on their research and experience. External users can also submit suggestions for additions. This sub-section is targeted towards the academic community.

The most recent addition to the COVR Toolkit is the Legal Tool. It is designed to provide guidance to users on the legal and legislative aspects of cobots. It is strongly based on the output of the COVR Award "LEGARA", which is white paper published by the law firm CO:Play [START_REF] Jønsson | COVR -Legal Risk Assessment (LEGARA) -White Paper[END_REF]. The Legal Tool covers topics such as the European legislative landscape, CE marking, liability for accidents, and legal risk assessments. Interestingly it also explains the legal value of voluntary documents such as standards or protocols.

For stakeholders with a lower level of robotics expertise, the COVR Toolkit also features a frequently asked questions (FAQ) section. Here key terms and concepts are explained.

IV. COVR AWARD PROJECTS AND TOOLKIT USAGE

The COVR Project provided over 5 million Euros in funding to third parties (so-called COVR Awards) to act as beta testers of the Toolkit while developing their own cobot application. They were asked to use the Toolkit and protocols in order to identify gaps and errors, or to add their specific domain knowledge to the Toolkit. In total, 60 projects featuring 108 organizations from 14 European countries were funded. The organizations included 40 technology developers, 36 universities/research organizations, 14 end-users, 12 integrators, and 6 other types of organizations. While the majority of the COVR Awards were focused on the domains, of manufacturing, healthcare, rehabilitation, and logistics, there was a long tail of other projects, focusing on application areas such as recycling, professional services, agriculture, construction, and food service.

In this section, we will briefly present three COVR Awards, highlighting their usage and feedback to the Toolkit. In particular, by way of example, we report about projects that feature a collaborative robot for different domains of application (specifically industrial, agriculture, and rehabilitation). Table 1 summarizes the key attributes of the three projects, leading to the following observations:

-Similar robotic devices can be used in a variety of applications and domains with very different associated standards; -Depending on the application and the domain, safety issues can be different and require specific strategies; -The COVR Toolkit and, in particular, the "skill-based" transversal approach are appropriate to deal with the different issues and challenges.

A. CoSMo Project 1) Introduction of partners, project goals, relevant domain

The CoSMo project aimed to enable system integrators to efficiently perform the risk assessment for a potentially large number of cobot applications in industrial manufacturing. In particular, the solution focused on how an applications engineer can review and update the risk analysis of a robotic cell after it has been adapted to manufacture new products. The project included two partners, namely the CoR-Lab of Bielefeld University and the Weidmüller Interface GmbH & Co. KG. The CoR-Lab is interested in topics related to cognitive and social robots that interact intuitively with humans, while Weidmüller is a leading company in industrial connectivity. Weidmüller uses modular cobot workstations (see Fig. 3) to produce various types of terminal blocks from the product range. A dedicated goal is to minimize the time required for setting up and adjusting the machine to new products. This is achieved by using exchangeable workspaces, which can produce different types of terminals (see Fig. 3, right, Products). The robot with its attached gripper tool moves along the individual stations (max. 4) of the workspace (see Fig. 3, Workspaces 1-4) and is primarily used for material handling. An exemplary assembly process is explained in the following: THMS-0386 a terminal insert is assembled from station to station and pressed into the terminal housing at the last station by using a pneumatic pressing device. The finished terminal is inspected by a camera and, if free of defects, is placed in the cardboard box. Otherwise, it is placed in the container for defective terminals via a metal chute. Then the process starts all over again. When the cardboard box is full, it is pushed via chute to a printing device, which prints the contents of the box on the label. The carton then slides over the carton chute to the removal point. If a different type of terminal can be produced by the already installed workspace, only the program of the production process is adapted. Otherwise, the modular workspace must be replaced, and the corresponding robot program must be loaded.

The basic frame of the machine is made of aluminum profiles. Furthermore, a UR3e robot from Universal Robots, a gripper from Zimmer, a camera from Cognex, a pressing device, a PLC, a touch display, and a Weidmüller workspace module are installed. Additionally, there are two carton chutes (infeed and outfeed of the machine) and a printing device for marking the carton labels.

The Universal Robot control software was used to program the UR3e robot arm, which also controls the connected gripper. The PCL is programmed with U-Create Studio, while Proconweb is used for the visualization of the touch display, which are software tools developed by Weidmüller. For quality control, the Cognex Insight Explorer camera software was used.

In general, the application has a high degree of variability. One workstation can be physically composed with different workspaces to produce a set of different products. The geometrical properties of the products combined with the properties of the tool (while being grasped and handled by the cobot), may result in potentially different risks.

2) Project outcome CoR-Lab and Weidmüller did a comprehensive literature survey on normative safety requirements for modularized workstations to review the current state of the art and regulations for such installations. Based on this survey, a semiautomatic process as illustrated in Fig. 4 was sketched in the CoSMo project, extending on earlier ideas by [START_REF] Awad | Integrated risk assessment and safety consideration during design of HRC workplaces[END_REF], [START_REF] Vicentini | Safety Assessment of Collaborative Robotics Through Automated Formal Verification[END_REF], [START_REF] Inam | Risk Assessment for Human-Robot Collaboration in an automated warehouse scenario[END_REF], and [START_REF] Saenz | Survey of methods for design of collaborative robotics applications-Why safety is a barrier to more widespread robotics uptake[END_REF] to reduce the effort of a risk analysis for cobot workstations. This process is based on the hypothesis that through the modularity of Weidmüller's robot workcell the complexity of the cobot workstation can be constrained by a dimension reduction that maps essential safety-relevant properties such as sharp geometries, edges and narrow distances of the modular elements as well as their composition into an abstract design space that allows simulation and reasoning. This space covers only the safety-relevant aspects required for the risk analysis, such as the geometrical abstractions, the typical human behavior, the robot behavior, and the overall (assembly) process. In that space, risks are analyzed, and risk reduction measures are applied in an iterative process to optimize the design configuration, until only acceptable risks remain. Finally, the physical set-up is adapted to the optimized abstract design and the COVR Protocols can be used to verify the risks and the effectiveness of the RRMs.

Within the CoSMo project, a model-based simulation of the cobot workstation for a representative manufacturing process at Weidmüller and a risk analysis plugin were developed in Visual Components (VC) to interactively support hazard identification and risk mapping. Using knowledge about the safety properties of the modular elements of the system, the prototypical plugin supports to some extent an automatic analysis of potential risks for a particular configuration of the modular cobot workstation. For instance, an automatic process can be applied to find risks that are caused by not keeping minimal distances between the robot's tool and the elements with associated hazards in the workstation.

Fig. 4. Risk analysis process using composable safety models for risks and risk reduction measures.

Following these ideas, the Award beneficiaries believe that the safety of modular cobot workstations can be analyzed with respect to their safety aspects already during the digital design phase. This reduces the amount of necessary real-world construction iterations. In addition to that, the automatic hazard identification and risk analysis processes might lead to an automatic classification of configurations that stay within the already analyzed safety envelope of the system under nominal conditions.

3) Toolkit usage and feedback

In order to check the biomechanical limit values, there is currently no other option than to perform the corresponding measurements according to the protocols. Even with the usage of the developed risk analysis approach, the measurements require a comparably high personnel effort and are timeconsuming. More practical would be a model-based simulation and analysis tool that could make the physical measurements obsolete. Further, the CoSMo consortium asked to add references in the protocols regarding the information on the measuring instruments, provided in the COVR Toolkit under "Test Equipment".

From the CoSMo perspective, the COVR Toolkit offered an excellent entry point for the safe design of cobot applications. The Document Finder is easy to use and provides a good overview of the guidelines, standards and protocols, thus, offering a suitable introduction to the subject for beginners. The protocols guide the user in a structured way through the measurement and show what to pay attention to. Following up on the ideas developed in CoSMo, strategies dealing with high variability, entailing many measurements and information on THMS-0386 how to deal with hazardous situations that cannot be measured due to the dimensions of the measuring instruments or a contact area that is too large seem to be required for the manufacturing applications considered in this project.

B. Hydrocobotics Project 1) Introduction of partners, project goals, relevant domain

The Hydrocobotics project was aimed at the development of a safe cobotized solution to support operators in dealing with an indoor hydroponic culture. The issue belongs to the agricultural domain from a perspective still not covered by the standards, mainly characterized by the indoor environment. The solution was developed through the collaboration of three main partners: Hepenix Ltd. as a robot integrator, SZTAKI research and technology organization and Green Drops Farm Ltd. As an enduser focused on the commercialization of hydroponic culture solutions. The base hydroponic system "Green Drops Hidropónia" developed by Green Drops Farm consists of a modular tower with an internal pump circulating the liquid; the modules have several cultivation layers, which feature holes for the insertion of mesh pots for plant cultivation. The tower can rotate within its base vase, permitting access to all the plants from one side by rotating the tower. The robotic apparatus consists of a serial manipulator universal Robots UR5 equipped with a Robotiq 2F-85 gripper and integrated onto an automated guided vehicle (AGV). This mobile cobot is able to move and operate autonomously in a workspace in which several Hidropónia towers may be included (see Fig. 5). Essential customized addons of the robotic solution are: i) a dock frame to be used as a reference for the positioning of the mobile robot with respect to the Hidropónia tower, ii) specific plant supports (so called "clips") for the robotized manipulation of the plants and iii) latch-based locking systems to fix the angular position of the tower during the manipulation of the plants. The obtained mobile robot is expected to move around in the workspace, approach the Hidropónia tower, and rotate the tower in order to access to any of the cultivation holes. Besides the development of a suitable and reliable robotic solution, the focus of the project was to assess the safety of the application, aiming at safe human-robot interaction.

2) Project outcome As an application belonging to the agricultural domain, but characterized by an indoor environment, there are currently no known domain-specific Type-C standards. As this situation is common to several emerging cobot applications, the decision was made to instead consider industrial robotics Type-C standards (i.e. ISO 10218-1 [START_REF]Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 1: Robots; International Organization for Standardization[END_REF], ISO 10218-2 [START_REF]Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 2: Robot Systems and Inte-gration; International Organization for Standardization[END_REF], ISO 3691-4, ISO/TS 15066). The risk assessment identified a series of hazards as the most critical from the perspective of workspace sharing and human-robot collaboration. These were analyzed and mitigated by appropriated risk reduction measures, as described in the following sub-sections. i) Finger pinch hazard with gripper.

A careless operator may operate with their hands too close to robot gripper; due to the bar linkage-based structure of the gripper, the closing motion may pinch one or more operator fingers. This hazard was eliminated by applying a customized cover onto the gripper. ii) Finger pinch hazard during locking with latch.

The finger of the operator may be pinched when the robot closes a mechanical latch. This hazard was also eliminated by applying a customized cover, preventing the insertion of the finger in the hazardous space. iii) Finger crush hazard with latch during the rotation of the Hidropónia tower.

When the cobot rotates the tower, a hand or finger may be crushed between the two parts of the mechanical latch. As a first risk reduction measure, an electromagnetic force and displacement sensor was developed and equipped as a mechanical interface between the robot and the gripper. Moreover, the crush scenario was tested by referring to COVR protocol "Test robot arm for collision with fixed object THMS-0386 (crush)", by using a GTE KMG 500-75 force gauge and Fujifilm prescale films for biofidelic force and pressure measurements, in order to compare the results with the safety thresholds reported in Appendix A of ISO/TS 15066 (see Fig. 6). iv) Hand injury hazard during plant loading and unloading with the plant support.

This hazard is related to the possible impact of the plants with the hand during their manipulation performed by the robot. In this case, by using the same equipment of the test previously described, the COVR protocol "Test robot arm for collision with movable object (impact)" was considered. Although the results were significantly lower than the thresholds reported in [START_REF]Robots and Robotic Devices-Collaborative Robots; International Organization for Standardization[END_REF] in the case of plant removal, the tests related to plant insertion exhibited the occurrence of scratches due to the break of the plant support, with the consequence that the measured pressures were above the acceptable thresholds. To mitigate this risk and keep contact pressures low, the plant support was redesigned, in order to break cleanly in case of impact at a specific zone of the clip.

3) Toolkit usage and feedback

The Toolkit was a fundamental source for the development of the project. In particular, the force and pressure tests that were executed for the validation of the hazardous human-robot contact scenarios were based on two protocols available on the Toolkit. These validation protocols build on an emerging testing approach for the validation of human-robot contact in different scenarios. Due to the importance of these issues in the viewpoint of HRC implementations, the COVR research group devoted great effort to the analysis of those testing procedures [START_REF] Scibilia | Analysis of Interlaboratory Safety Related Tests in Power and Force Limited Collaborative Robots[END_REF], resulting in reliable and comprehensive validation protocols that deal with several human-robot contact scenarios.

C. DOROTHY project 1) Introduction of partners, project goals, relevant domain

The DOROTHY project was aimed at assessing the safety of a rehabilitation robot based on two industrial cobots. The REHAROB therapeutic system [START_REF] Peter | Exercising daily living activities in robot-mediated therapy[END_REF], which was the subject of the DOROTHY project has been developed in previous research projects and is based on two cobots which are generally used in industry. The two cobots attach to a patient's elbow and wrist to execute movements programmed by a therapist (Fig. 7). The system is intended to be used for upper limb therapy of hemiparetic patients. The physiotherapist teaches the equipment by performing the relevant movements on the patient, which are then recorded and repeated by the device. The project was executed by DARPAMOTION Ltd. and Budapest University of Technology and Economics from Hungary.

Since requirements and circumstances regarding safety are very different in medical applications compared to cobots in industry, the Award project focused on uncovering the challenges and possibilities of using industrial cobots for a medical device. Based on a risk assessment, a safety validation protocol was developed for one of the identified risks, namely the risk of exceeding the anatomical range of motion or overreaching.

2) Project outcome

The REHAROB system is not the only medical device based on industrial cobots. An analysis of commercial and noncommercial robot systems was executed, describing three commercial medical robot systems using industrial robots (CyberKnife, ROSA, YOMI), eight commercial medical robot systems using industrial cobots (ARTAS iX, Sculptura, CARLO, TMS-Cobot, Modus V, iYU Pro, EMMA, ROBERT) and seven non-commercial medical robot systems using industrial cobots (ALEX, RAINER, Cobot for Doppler Sonography, DynamicDentalArm, BROCA, Chinese robot dentist, inRehaRob). Some of the main results of this analysis were:

 Industrial cobots are frequently used for the development of new medical systems as they are low in cost and support fast prototyping  There is one medically certified cobot on the market, namely the KUKA LBR MED (KUKA AG, Augsburg, Germany). The advantage of using this medically certified cobot for new systems is the reduced need for engineering development time and testing. The disadvantages are the higher purchase cost compared to other industrial cobots and the undetermined intended use.  The development process of medical systems is as individual as their application. Industrial cobots can be a simple and economical way to develop a medical robot that moves objects or body parts freely in space and is constrained under contact impedance. One of the risks identified in the risk analysis was an unnatural joint angle in the wrist or elbow caused by improper trajectory calculation. During such a hazardous situation, the patient might suffer joint injuries. The Award beneficiaries decided to further investigate this risk and develop a safety validation protocol for the skill of limiting the joint's range of motion. To reliably assess the human joint angles created by the robot trajectory, an instrumented dummy representing the patient's arm was developed. The dummy arm is attached to a wooden frame at the dummy shoulder joint (Fig. 8). It consists of a 3D printed structure combined with rolling bearings with inner and outer aluminum bushings as joints.

The joints are equipped with rotary sensors and all necessary cables as well as the signal processing unit are routed though THMS-0386 the inside of the dummy. Brake plugs and push-buttons are implemented for safe transportation, zero offset calibration and alteration of the system to represent left-or right-side anatomy. To account for a wide range of patient anthropometrics, the arm length and shoulder height can be adjusted from the 5th percentile female to 95th percentile male using the adjustment options in the frame for shoulder height adjustment and spring pre-loaded latches for arm length adjustment. A safety validation protocol was then developed using this dummy arm and a self-developed data analysis sheet. The protocol user can determine the allowed ranges of joint motion and test a medical robot system's safety by employing the validation protocol in combination with the dummy and data analysis sheet. The joint ranges of motion are defined before the test as prohibited ranges and precaution zones. After the therapeutic motion exercises that are to be assessed were executed by the robot system connected to the dummy test rig, the data analysis sheet will present the protocol user with an overview of the movement durations in the riskless zone, precautionary zone and prohibited zone of all relevant joint angles. The safety validation is passed if the duration of prohibited zones in all angles is equal to 0%.

3) Toolkit usage and feedback

The Award beneficiaries used the Toolkit to collect information on cobot safety assessment and identified a missing link between device type and standards, which was then corrected.

An analysis of industrial cobot systems in medical systems was performed. A short summary of the analysis and a link to the complete analysis can be found in a case story on the COVR Toolkit.

The risk of overreaching and exceeding joint range of motion was identified as a risk that had not yet been addressed in the Toolkit protocols in combination with the robot type robotic arm. The Award beneficiaries then developed the safety validation protocol for this combination, defined the dummy arm as required test equipment, and provided the data analysis sheet as support for the protocol execution. This protocol and the information about the testing equipment are now available on Toolkit.

D. Feedback and standardization activities

COVR Awardees and all Toolkit users were asked to anonymously provide feedback on the Toolkit and protocols via custom-made online questionnaires. The Toolkit user survey has three main sections to 1) collect information about the respondent and their Toolkit usage, 2) to understand their satisfaction (via an abbreviated version of the Usability, Satisfaction and Ease of Use (USE) questionnaire [START_REF] Lund | Measuring Usability with the USE Questionnaire[END_REF]), and 3) to allow open-ended suggestions for improvement.

The questionnaires were used to systematically collect indepth information about the usage and user experience of the various COVR elements. We received 75 responses through the online Toolkit user survey. The Toolkit was reportedly used regularly, with the subsections "Directives & Standards", "Protocols" and "Risk Assessment" being viewed the most (Fig. 9).

Questions on whether and how much time was saved through using the Toolkit were introduced at a later point in time to the survey. Therefore, these questions only were answered by 37 respondents. Of these 37 respondents, 92% indicated that time savings were possible, with the majority estimating that the time saved is in the range of a few days (Fig. 10). The survey questions focusing on usability indicate a positive appreciation of the Toolkit, with none (0%) of the respondents reporting that they are never satisfied by the quality of the tools and documents in the Toolkit (Fig. 11). THMS-0386 Building on Tookit contents, two separate standardization activities were initiated in late 2020. At the European level, a CEN-CWA (CEN Workshop Agreement) titled "Guidelines for the development and use of safety testing procedures in humanrobot collaboration" was developed [START_REF]Guidelines for the development and use of safety testing procedures in human-robot collaboration[END_REF]. This document describes the motivation for cross-domain protocols and provides the overall structure of protocols so to support future developments. Furthermore, the the ISO/PAS (Publicly Available Specification) 5672 "Robotic devices -Test methods for measuring forces and pressures in human-robot contacts and collisions", focusing on the protocol for measurement of power and force limited robots, was developed and is currently in the final commenting and approval phases. These activities have resulted in hundreds of individual comments from leading international experts on the concepts of the protocols and safety skills, as well as on the contents of specific protocols. The CEN-CWA was published in January 2022, and the ISO/PAS is expected to be published by the end of 2022.

V. CONCLUSION

This paper deals with the topic of ensuring safety for new robotics applications featuring HRC. The cross-domain nature of robotics increasingly makes it difficult for robotics end-users and system integrators to identify relevant requirements, standards, and regulations.

The EU-funded project COVR has developed a web-based Toolkit designed to offer stakeholders with a wide range of background knowledge and expertise support along various phases of the development lifecycle. The Toolkit builds on the concept of safety skills to offer users a high degree of certainty regarding the relevant health and safety requirements, as well as practical guides for how to execute a risk analysis and how to perform system level validation measurements with various types of measuring equipment.

A major goal of COVR is building consensus with all relevant stakeholders, across Europe and the world. As new technologies are emerging, the regulatory background will develop and existing standards will be modified or new documents will be developed. It is therefore pivotal to maintain and update the Toolkit. To address this challenge as the project ends, a community of so-called COVR Hubs is being created. The COVR Hubs members will maintain the Toolkit, offer services related to HRC validation and share best practices to ensure sustainability and continued engagement with the protocols in the near future. Through the COVR Hubs we will be able to continue the discussion with relevant stakeholders from standardization, occupational and health insurance, safety verification bodies, and national regulation agencies, and the robotics community.

Fig. 3 .

 3 Fig. 3. Overview of the cobot workstation, different potential workspaces, and products associated with them. The various combinations can lead to potentially different hazards for a human working collaboratively in the workstation.

Fig. 5 .

 5 Fig. 5. a) Two versions of the developed clips for automated manipulation of the plants and b) overview of the collaborative application.

Fig. 6 .

 6 Fig. 6. Impact test simulating the finger crush hazard with latch during rotation of the Hidropónia tower, with covers for risk reduction highlighted.

Fig. 7 .

 7 Fig. 7. Schematic drawing of the dual cobot arm therapeutic system.

Fig. 8 .

 8 Fig. 8. Visualization and photo of the dummy limb test rig.

Fig. 9 .

 9 Fig. 9. Toolkit sections that were used or browsed by the respondents.

Fig. 10 .

 10 Fig. 10. Estimations regarding the time saved by using the Toolkit.

Fig. 11 .

 11 Fig. 11. Pie chart of questionnaire results regarding overall satisfaction with the COVR Toolkit.

ACKNOWLEDGMENT Authors are grateful to Janos Nacsa, David Szur and Kristof Abai from SZTAKI, Jozsef Toth from Hepenix Ltd and Greta Rácz from Green Drops Farm Ltd for their professional and successful development of the Hydrocobotics project. Authors are thankful to Dennis Wigand and Stefan Krüger from Bielefeld University and Mark Edler from Weidmüller Group for their professional consultancy and support of the CoSMo project. Authors also thank Szabolcs David and Al-Absi Ghassan Mohammed Ahmed Kaid from BME, Mark Otto Bauer and Tamas Patai from DARPAMOTION Ltd for their professional contribution to the DOROTHY project. I. P. Author thanks for the ED_18-2-2018-0006 grant on "Research on prime exploitation of the potential provided by the industrial digitalisation".

This work has been carried out within the framework of the European Project COVR, receiving funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 779966.


Juliette Mattioli
email: juliette.mattioli@thalesgroup.com

Henri Sohier
email: henri.sohier@itr-systemx.fr

Agnès Delaborde
email: agnes.delaborde@lne.fr

Gabriel Pedroza
email: gabriel.pedroza@cea.fr

Kahina Amokrane

Afef Awadid
email: afef.awadid@irt-systemx.fr

Zakaria Chihani
email: zakaria.chihani@cea.fr

Souhaiel Khalfaoui
email: souhaiel.khalfaoui@valeo.com

Kahina Amokrane-Ferka
email: kahina.amokrane-ferka@irt-systemx.fr

Towards a holistic approach for AI trustworthiness assessment based upon aids for multi-criteria aggregation

Keywords: Trustworthiness Assessment, Trustworthiness Attributes, Trustworthiness Metrics and Key Performance Indicators (KPIs), Multi Criteria Decision Aid, Data Quality, Robustness, Explainability SafeAI

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Without an accompanying assessment of trustworthiness from the early stages of development, the deployment of an Artificial Intelligence (AI) component within a safetycritical systems such as in avionics, mobility, healthcare and defense becomes risky.

Trustworthiness definition

Trust is the willingness of one party to perform certain actions that are important to stakeholders (AI scientist, safety engineer, certification auditor, end-user, etc.) regardless of the other partys ability to monitor or control [START_REF] Mayer | An integrative model of organizational trust[END_REF]. Trust is defined [START_REF]Systems and software engineering -Systems and software Quality Requirements and Evaluation (SQuaRE) -System and software quality models[END_REF] as "the degree to which a user or other stakeholder has confidence that a product or system will behave as intended". But, the trust literature distinguishes trustworthiness (the ability, benevolence, and integrity of a trustee) from trust (the intention to accept vulnerability to a trustee based on positive expectations of his or her actions) [START_REF] Colquitt | Trust, trustworthiness, and trust propensity: a meta-analytic test of their unique relationships with risk taking and job performance[END_REF]. Trustworthiness is represented as an objective aspect of trust estimated based on evidences or observations; whereas trust includes subjective aspects of a cognitive entity's opinion such as a human. We consider the following definition: trustworthiness (ISO/IEC DIS 30145-2) is the "ability to meet stakeholders' expectations in a verifiable way". Moreover, [START_REF] Standards | Technology, Us leadership in ai: a plan for federal engagement in developing technical standards and related tools[END_REF] identified nine characteristics that define AI system trustworthiness: accuracy, reliability, resiliency, objectivity, security, explainability, safety, accountability, and privacy.

Trustworthiness attributes

Trustworthiness is a complex concept which can be broken down into different attributes. In 1977, a FAA (Federal Aviation Administration) panel dedicated to how to certify aircraft as airworthy, explicitly linked the notion of trustworthiness to accounting. Then, security and dependability became key system attributes [START_REF] Cho | Stram: Measuring the trustworthiness of computer-based systems[END_REF] to assess the trustworthiness of a computer-based system: Avizienis et al. [START_REF] Avizienis | Basic concepts and taxonomy of dependable and secure computing[END_REF] used dependability to represent the overall quality measure of a system based on four sub-attributes including security, safety, reliability, and maintainability. To depict dynamic system security metrics, Pendleton et al. [START_REF] Pendleton | A survey on systems security metrics[END_REF] presented a cyber-security metric framework based on the interactions between attackers and defenders. By taking into account a variety of probabilistic and/or analytical models to undertake a quantitative evaluation, Ramos et al. [START_REF] Ramos | Model-based quantitative network security metrics: A survey[END_REF] primarily examined model-based network security indicators. Gol Mohammadi et al. [START_REF] Mohammadi | Trustworthiness attributes and metrics for engineering trusted internet-based software systems[END_REF] proposed a first list of software trustworthiness attributes. Fig. 1 shows the evolution of the number of publications on different attributes. [START_REF] Pipino | Data quality assessment[END_REF] explores the potential benefits and challenges of quantifying AI risk by discussing how such an assessment could improve AI regulation.

These attributes must be mapped onto the AI processes and life-cycle, keeping track of their relations with the different stakeholders. Indeed, the definition of trustworthiness is up to individual interpretation and preference. For instance, to feel confident in the handling of safetycritical data, end-users can be concerned with usability.

Trustworthiness management

Trustworthiness does not only emerge from the product itself, but also from the process (how the product was made), the tools and infrastructure (with what), the people (by whom) as well as the governance (who decides). Trustworthiness can also be considered from a quality point of view or a risk point of view. In the former, the chances to meet the stakeholder expectations are maximized (by good practices and clear metrics). In the latter, the chances not to meet the stakeholder expectations are minimized (by identifying and mitigating potential issues). Thus, all trustworthiness attributes can generally be considered from a quality or risk point of view. Quality is at the center of the SQuaRE (Systems and software Quality Requirements and Evaluation) series of standards ISO/IEC 25000:2014 and AI quality is more specifically considered in ISO/IEC DIS 25059 (under development).

The principles of risk management are explained in ISO 31000:2018 and AI risk is more specifically considered in ISO/IEC FDIS 23894 (under development).

AI heterogeneity

Trustworthiness assessment should be adapted to the different natures of AI systems. AI can follow three major paradigms: 1) Data-driven AI, which includes statistical and connexionist AI suitable for pattern matching and recognition, classification and forecasting problems; 2) Knowledge-based AI (also called Symbolic AI) relies on knowledge representations such as ontologies and conceptual graphs. Real world information is transformed into something understandable and usable by machines so that decisions can follow an organized path of planning, solution searching and optimization; and 3) Hybrid AI which is more than a combination of symbolic AI and machine learning approaches by encompassing any synergistic combinations of various AI techniques, which could be enhanced by a priori knowledge (such as mathematics, physics or geometry).

Not only should trustworthiness assessment be adapted to these paradigms, but also to the various AI functions such as anomaly detection (on images or time series), forecasting, decision making under uncertainty, planing and scheduling problems, optimization under constraints, etc. Trustworthiness assessment is field dependant (mobility, healthcare, finance, etc). Thus, an adaptation effort is highly requested.

Can trustworthiness be measured?

The quantification of AI-based system trustworthiness has become a hot topic [START_REF] Braunschweig | The wall of safety for AI: approaches in the confiance.ai program[END_REF]. From a strict metrological point of view, measurement is relative to a physical property which can be compared to a reference quantity of the same kind. Following this definition, trustworthiness cannot be "measured". However, each attribute related to trustworthiness can be represented on a scale (e.g., number scale, nominal scale, ordinals scale) [START_REF] Adam | Towards the engineering of trustworthy AI applications for critical systems -The Confiance[END_REF]. A trustworthiness metric can be defined as objective, mathematical measure of the AI-based component/system that is sensitive to differences in safety critical characteristics. It provides a quantitative measure of an attribute which the body of solution exhibit. For example, estimating the trustworthiness of a system can rely on performance and/or quality scoring (e.g., for reliability: Fleiss Kappa score, goodness-of-fit tests, or for accuracy: precision, recall, F-score, etc). However, trustworthiness is not only based on objective attributes -for example, usability and interpretability are linked to human judgment; trustworthiness assessment should then also include rigorous methodological processes to manage subjectivity.

Unified approach to support trustworthiness assessment

Multi-Criteria Decision Aiding (MCDA) is a generic term for a collection of systematic approaches developed specifically to help one or several Decision Makers (DM) to assess or compare some alternatives on the basis of several criteria [START_REF] Labreuche | A general framework for explaining the results of a multi-attribute preference model[END_REF]. The difficulty is that the decision criteria are frequently numerous, dependent and sometime conflicting. For example, effectiveness may be conflicting with robustness, explainability, or affordability. The viewpoints are quantified through attributes (see §2.1). First, to assess AI trustworthiness, the choice of the relevant attributes is not easy, since the selection pertains to the context of application, which is modeled according to several elements (Operational Design Domain, intended domain of use, nature and roles of the stakeholders, etc.) The attributes can be quantitative (typically numerical values either derived from a measure or providing a comprehensive and statistical overview of a phenomenon) or qualitative (based on the detailed analysis and interpretation of a limited number of samples). Then once the list of relevant attributes has been defined, the aggregation of several attributes remains complex due to commensurability issues: indeed, this is equivalent with combining "oranges and apples", none of the attributes having the same unit. In addition, one aims at making trade-offs and arbitration between the attributes. This means that the value of each attribute should be transformed into a scale common to all attributes and representing the preferences of a stakeholder, and that the values of the scales for the different criteria should be aggregated. These elements constitute the main steps for solving the problem using an MCDA approach.

Aggregation functions are often used to compare alternatives evaluated on multiple conflicting criteria by synthesizing their performances into overall utility values [START_REF] Grabisch | A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid[END_REF]. Such functions must be sufficiently expressive to fit the DM's preferences, allowing for instance the determination of the preferred alternative or to make compromises among the criteria -improving a criterion implies that one shall deteriorate on another one. MCDA provides a tool to specify the good compromises [START_REF] Labreuche | A general framework for explaining the results of a multi-attribute preference model[END_REF].

Our approach is based on the following steps (see fig. 2):

1.

Step 1: Structuring attributes in a semantic tree; 2.

Step 2: Identification of numerical evaluations; 3.

Step 3: Adapting attributes for commensurability; 4.

Step 4: Definition of an aggregation methodology to capture operational trade-offs and evaluate higher-level attributes.

Step 1: Semantic tree

Based on different sources (norms, standards, scientific communications, industrial and institutional reports, Confiance.ai reports, etc.), the characterization and evaluation of trust attributes focus on defining and structuring the attributes that constitute trust in the context of AIbased safety critical systems [START_REF] Pons | Priority quality attributes for engineering AI-enabled systems[END_REF] going beyond a risk analysis as proposed in [START_REF]High-Level Expert Group on Artificial Intelligence, Assessment List for Trustworthy Artificial Intelligence (ALTAI)[END_REF][START_REF] Piorkowski | Quantitative ai risk assessments: Opportunities and challenges[END_REF].

Our problem of assessing Trustworthiness is decomposed in several sub-problems by introducing a hierarchy of an important number of specific criteria. This structuring phase aims to construct a tree representing a hierarchy of points of view in which the root represents the overall evaluation, and the leaves are the elementary attributes. In order to produce such a hierarchy, one shall succeed in grouping the criteria according to a classification that makes sense for the stakeholders. At the end of this step, one shall obtain the relevant criteria together with their organization in a tree. This first step has been captured in the mind-map of Fig. 3.

The attributes are currently grouped according to the capabilities they characterize: technology, ethics, interaction and trust intermediaries (such as certification).

Technology is system-centric, it refers to the ability to verify that the AI-based component has valid and robust intrinsic properties such as accuracy, robustness, safety and security. Thus, AI-based systems should generate accurate output as consistent as possible with the ground truth. Additionally, AI systems should be robust to changes, specifically in complex, dynamic and uncertain real environments. Moreover, AI programs or systems must not harm any human being under any circumstances that prioritize user safety. In addition, the autonomy of trustworthy AI should always be under user's control. In other words, it has always been From interaction's perspective, trustworthy AI should possess the properties of usability, explainability and interpretability. Specifically, AI-based systems should not cease operation at inappropriate times (e.g. at times when the lack of output could lead to safety risks), and these programs or systems should be easy to use for people with different backgrounds. Trustworthy AI solutions should allow explanation and analysis by humans to reduce potential risks and harms and empower human users. In addition, trustworthy AI should be transparent so people can better understand its mechanism.

Ethics, strongly linked in Europe to the notion of fundamental rights, is notably put forward in the work of the AI HLEG (High-Level Expert Group on Artificial Intelligence) of the European Commission [START_REF] Hleg | Assessment list for trustworthy artificial intelligence (altai) for self-assessment, High Level Expert Group on Artificial Intelligence[END_REF]. A system must, for example, be law-abiding, fair, accountable, environmentally friendly and compliant with the user privacy. Specifically, AI systems should operate in accordance with all relevant laws and regulations, as well as with the ethical principles of human society.

As some notions (e.g. explainability) concern several dimensions (ethics vs. interaction), Confiance.ai program made an arbitrary choice to be consistent with the methodology. Finally, the attributes for trusted ecosystem intermediaries focus on the relationships to thirdparties, in particular quality assurance, audit and certification activities. All these properties apply to the AI-based component and the system that embeds AI, but they also apply to the quality of the data used for training connectionist AI and/or to the quality of knowledge representation and reasoning used in symbolic AI.

Step 2: Numerical evaluations

All nodes return a numerical evaluation. Specific Key Performance Indicators (KPI), metrics or evaluation methods are used to qualify the leaves of the tree according to the use cases. For example, data quality is a problem that has been studied for several decades now [START_REF] Mattioli | Information quality: the cornerstone for AI-based industry 4.0[END_REF]. However, primarily the focus has been on the data in operational databases and data warehouses. Now, Data-driven AI is generating renewed interest in data quality, but there is yet no consensus on what comprises the data quality characteristics. Thus, [START_REF] Wang | Beyond accuracy: What data quality means to data consumers[END_REF] were among the first arguing that limiting quality to the level of accuracy is not enough, highlighting that the level of quality for given data can depend on its purpose. Its principles require an assessment of the various quality attributes as presented in §3.2, mainly in fig. 6. Standards are currently being developed to define data quality attributes for ML (Machine Learning): ISO/IEC CD 5259-1 (terminology and principles) and ISO/IEC CD 5259-2 (data quality measures).

Step 3: Commensurability

Aggregating different attributes for a global assessment requires that they are commensurate. This implies that one shall be able to compare any numerical evaluation of an attribute with any numerical evaluation of any other attribute. In order to make the assessment "comparable, " sound methods for normalization (to make the comparison between variables comparable) have to be applied to single variables in order to first make them comparable, that is, transforming the various scales of variables into one unique scale. The numerical evaluation of the attributes is thus encoded in the [0, 1] interval where the value 0 corresponds to the total absence of the property beneath a trustworthiness criterion, and value 1 corresponds to the complete satisfaction of the criterion. The normalized indicators could be aggregated using specific formulas (e.g. min/max, arithmetic mean, weighted sum, etc.). If one attribute is more "important" than another with respect to stakeholder preference, the former is assigned a stronger weight than the latter within the aggregation procedure.

Step 4: Aggregation and trade-off

The global assessment would be made on the basis of several trustworthiness attributes denoted by 𝑁 = {1, ..., 𝑛}. The proposed approach provides a tool to identify best compromises from the stakeholder point of view. Each attribute 𝑖 ∈ 𝑁 is quantified by a KPI -also called metric or Radar chart is a visual method for comprehensive evaluation, particularly useful for holistic and overall assessment through multivariate data. However, this representation does not allow understanding the interactions and dependencies between attributes.

The goal of MCDA is to define a numerical representation of the preferences of the stakeholders, expressed as a function 𝑢 : 𝑋 → [0, 1]. The function will be used to compare each alternative or assess each alternative's level of satisfaction in order to provide the overall level of satisfaction for each. As mentioned before, the scale [0, 1] can be interpreted as a degree of satisfaction. It is classical to write u in a decomposed way : 𝑢(𝑥) = 𝐹 (𝑢1(𝑥1), ..., 𝑢𝑛(𝑥𝑛)), for all 𝑥 ∈ 𝑋, where 𝑢𝑖 : 𝑋𝑖 → [0, 1] is a utility function (also called value functions) and 𝐹 : [0, 1] 𝑛 → [0, 1] is called the aggre-gation function where 𝐹 (0, ..., 0) = 0, 𝐹 (1, ..., 1) = 1, and 𝐹 (𝑥1, ..., 𝑥𝑛) ≤ 𝐹 (𝑦1, ..., 𝑦𝑛) if 𝑥𝑖 ≤ 𝑦𝑖, ∀𝑖. Moreover, the utility function normalizes the metrics and provides a measure of satisfaction for a single metric. The aggregation function takes a normalized score as input and returns an aggregate score. We recommend using the MACBETH approach [START_REF] Bana E Costa | A theoretical framework for Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH)[END_REF][START_REF] Bana E Costa | A multicriteria decision analysis model for faculty evaluation[END_REF] to develop utility functions for resolving past difficulties related to interval scale construction and compatibility issues in a way that fully satisfies stakeholders. and mathematically significant.

The most widely used aggregation function is the weighted sum. It assumes the independence among the criteria. This is a major limitation as criteria often interact. We need to use other type of aggregation function such as the Choquet integral [START_REF] Choquet | Theory of capacities[END_REF][START_REF] Sun | Aggregation functions considering criteria interrelationships in fuzzy multi-criteria decision making: state-of-the-art[END_REF], which is an extension of the weighted sum that is capable of measuring the influence of the importance of the individual criteria and the importance of the interrelationships among criteria.

Today, this step is work in progress.

Focus on Data-driven AI

In real-world industrial settings, the data-driven AI model is only a small part of the overall system and significant additional engineering and system functionalities are required to ensure that the model can operate in a reliable, predictable and scalable way with proper engineering of data and model pipelines, monitoring and logging, etc. While the necessity and usefulness of reasoning about trust assessment is obvious, obtaining trustworthiness scores remains a challenging task. To illustrate such issues, as stated previously, some aspects linked to trustworthiness are highly subjective or context dependent. For example, the notion of "data quality" (resp. "robustness", "explainability") requires having a knowledge of all induced attributes including those that are system dependent such as data availability, data portability, data precision, etc. (resp. adaptability, durability, resilience, etc.). The subjectivity or vagueness of the attribute definitions does not always represent a major hindrance to use them in operational settings, because skills and knowledge of AI and safety engineers may be enough to determine what may be appropriate thresholds and scores.

Classification performance

Classification is a prediction type used to give the output variable in the form of categories with similar attributes. Some of the popular metrics for the assessment of classification are Accuracy, Precision, Recall, F1 Score... Confusion Matrix is a core element that can be used to visualize the performance of the ML classification model, but it is a tool rather than a metric. By nature, it is a table A precision score (see fig. 5) close or equal to 1 will signify that your model did not miss any true positives, and is able to classify well between correct and incorrect labeling of observed data. Recall is the proportion of actual positives that the model has correctly identified as such out of all positives. Specificity is the proportion of actual negatives that the model has correctly identified as such out of all negatives. A high F1 score symbolizes a high precision as well as high recall. It presents a good balance between precision and recall and gives good results on imbalanced classification problems. The ROC Curve is a plot which shows the performance of a binary classifier as function of its cut-off threshold. It essentially shows the 𝑇 𝑃 rate against the 𝐹 𝑃 rate for various threshold values. Selecting the most suitable evaluation metric strongly depends on the way how the stakeholder defines the criticality of the application.

Data quality

Data quality is at the center of the standard ISO/IEC 25012:2008. The standard distinguishes between "nherent data quality" and "system-dependent data quality". The former is intrinsic to the data and does not depend on the application (e.g. correctness). The later is application specific (e.g. accuracy). Data quality can also be considered for a complete data set (e.g. completeness) or for a unique value (e.g. currentness). For example, some data quality characteristics are described in fig. 6.

Traditionally, metrics for data accuracy are based on the rate of correct data items over an entire data set, using a 1 for an accurate data item, and a 0 otherwise: 𝑑𝑎𝑡𝑎_𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = ∑ 𝑁 𝑖=1 𝛼(𝑑𝑖)/𝑁 where 𝑁 is the number of data elements in the dataset, and 𝛼(𝑑𝑖) is 1 if data element 𝑑𝑖 is correct, and 0 otherwise.

The assessment of the data timeliness attribute [START_REF] Batini | Data and Information Quality[END_REF] indicates whether the data was submitted in due time, respecting the data gathering deadline:

𝑑𝑎𝑡𝑎_𝑡𝑖𝑚𝑒𝑙𝑖𝑛𝑒𝑠𝑠 = 𝑚𝑎𝑥 ( 1 -
age of the data value shelf life

) 𝑠
where "age of the data value" represents the time difference between the occurrence (i.e., when the data value was created) and the assessment of timeliness of the data value; "shelf life" is defined as the maximum length of time the values of the considered attribute remain up-todate, which can be determined through expert knowledge. Thus, a higher value of the parameter shelf life implies a higher value of the metric for timeliness, and vice versa.

The exponent 𝑠 > 0, which has to be determined based on expert estimations, influences the sensitivity of the metric to the ratio (age of the data value / shelf life).

The data completeness metric could be based on the Ge and Helfert's ratio [START_REF] Ge | A framework to assess decision quality using information quality dimensions[END_REF], and defined as: 𝑑𝑎𝑡𝑎_𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑛𝑒𝑠𝑠 = ∑ 𝑁 𝑖=1 𝛾(𝑑𝑖)/𝑁 where 𝛾(𝑑𝑖) is 0 if 𝑑𝑖 is a missing data, and 1 otherwise.

Data correctness could be defined as: 𝑑𝑎𝑡𝑎_𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑛𝑒𝑠𝑠 = 1/(1 + 𝑑(𝜔, 𝜔𝑚)) where 𝜔 is the data value to be assessed, 𝜔𝑚s the corresponding real value and 𝑑 is a domain-specific distance measure such as the Euclidean distance or the Hamming distance. A larger difference between 𝜔 and 𝜔𝑚 s represented by a larger value of the distance function, which in turn leads to a larger denominator and thus a smaller metric value.

Robustness

The IEEE glossary of software engineering [START_REF]ANSI/ IEEE Std[END_REF] defines robustness as "The degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions". Related terms are thus error and fault tolerance, the first one regarding error in data inputs whereas the second at component level. In the present context, the robustness of an AI-enabled system essentially depends and is focused on the ML or DL (Deep Learning) components and the phase in the development cycle where the training model is designed and tested (i.e. the phase involving ML/DL algorithms, training and testing data sets). As it has been highlighted in [START_REF] Szegedy | Intriguing properties of neural networks[END_REF], ML/DL models exhibit counter-intuitive properties like (1) the misclassification of (adversarial) perturbations that are statistically indistinguishable from the ones in the training data set (e.g. identically distributed noises) and (2) the misclassification of data subsets representing a semantic unit/object in the presence of minor perturbations that break data regularity whereas still being readable by humans (e.g. small square in stop signal).

Characteristic Definition

Accuracy

The degree to which the data has attributes that correctly represent the true value of the intended attribute of a concept or event in a specific context of use.

Completeness

The degree to which subject data associated with an entity has values for all expected attributes and related entity instances in a specific context of use.

Consistency

The degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use.

Correctness

Degree to which the data is free from errors.

Currentness

The degree to which data has attributes that are of the right age in a specific context of use.

Representativeness

Degree to which the data is representative of the statistical population. Overall, a definition allowing to test the robustness of a model 𝑀 is the measure of the impact of the minimum adversarial perturbation across many samples 𝑥. In addition, robustness can (should) be tested at two levels of possible perturbations as follows [START_REF] Zhang | Machine learning testing: Survey, landscapes and horizons[END_REF]:

Local
) ≤ 𝛿 ⇒ 𝑀 (𝑥) → 𝑀 (𝑥 ′ ).
If the model outputs 𝑀 (𝐷) conform a dense set allowing a distance metrics 𝑠(.), the convergence can be validated for a given 𝜀 > 0 satisfying 𝑠(𝑀 (𝑥), 𝑀 (𝑥 ′ )) < 𝜀. In practice, such post-condition could be difficult or unfeasible to verify depending upon the nature of 𝑀 (𝐷).

Further means are thus needed to understand how perturbations impact misclassification.

Explainability

Explainability is by far one of the most rich and complex to assess feature in recent research concerning ML/DL topics. Several reasons justify such fact, in particular because explainability aims to provide answer as to why ML/DL algorithms succeed or fail, which is rather a challenge given their heuristic nature and intricacy. Explainability is also a high-level demand in several domains aiming to transfer safety-critical human-based tasks to autonomous systems, e.g. for accountability purposes, a basic explainability requirement for a self-driving vehicle being to sufficiently characterize the contribution of the ML/DL network branches during pedestrian/obstacle detection. Last, yet not the least, explaining the behavior of ML/DL models can be tihgtly coupled to solve apparent conflicts/inconsistencies between certain attributes/features. To illustrate this point, we recall the counter-intuitive properties of a ML/DL model referred in [START_REF] Szegedy | Intriguing properties of neural networks[END_REF] which are rather related to robustness, e.g. misclassification of statistically indistinguishable points, misclassi-fication of the same points after adding new training data and updating model parameters, etc. Such apparently inconsistent outcomes make designers raise questions about ML/DL models' foundations and call for methods to characterize them. Thus, explainability is a term used to encapsulate and refer to all previous needs and aims and is therefore a qualitative attribute of AI-based systems. That said, there are no unified methods or scales to evaluate explainability. Recent surveys, as the one offered by [START_REF] Xie | Explainable deep learning: A field guide for the uninitiated[END_REF], suggest that explainability can be decomposed by the methods used to evaluate it. A brief description of the main families found in literature is provided below.

Visualization methods pursue the characterization of a ML/DL network by visual observation of the levels of activation/deactivation according to the input data and their influence in the classification performance, sensitivity, and other functional/structural properties. Representative instances in this family are: Back-propagation helps to observe relevance of data in terms of the activation/deactivation gradients observed at different layers in the network during training, e.g. Activation Maximization [START_REF] Erhan | Visualizing Higher-Layer Features of a Deep Network[END_REF], Deconvolution [START_REF] Zeiler | visualizing and understanding convolutional networks[END_REF], Layerwise Relevance Propagation [START_REF] Montavon | Explaining nonlinear classification decisions with deep taylor decomposition[END_REF]. Perturbation-based methods provide means to observe and compare its impact in the network w.r.t. nonperturbed input, e.g. Occlusion Sensitivity [START_REF] Zeiler | visualizing and understanding convolutional networks[END_REF], Representation Erasure [START_REF] Li | Understanding neural networks through representation erasure[END_REF], Meaningful Perturbation [START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF].

Distillation methods aim to represent (distill) the knowledge encoded in the ML/DL network after training via a more human-readable format suitable for both user interpretation and logic/machine reasoning. Some representative instances in this family are: Local Approximation methods mimic the input/output behavior of the target ML/DL model on smaller data sets, and using approximation functions, e.g. linear functions. Local Approximation methods are based upon the hypothesis that the ML/DL behavior can be better and more easily characterized on local areas rather than over the entire data set, e.g. LIME [START_REF] Ribeiro | Explaining the predictions of any classifier[END_REF], Anchors [START_REF] Ribeiro | Anchors: Highprecision model-agnostic explanations[END_REF]. Model Translation methods aim to mimic input/output behavior of the target ML/DL model however considering the whole data set over a symbolic model, e.g. Graphbased [START_REF] Zhang | Interpreting cnn knowledge via an explanatory graph[END_REF], Rule-based [START_REF] Harradon | Causal learning and explanation of deep neural networks via autoencoded activations[END_REF].

Intrinsic methods search to integrate the means for explainability as part of the design of the ML/DL model. The explainability of ML/DL networks should be intrinsic and thus input/output behavior should be explicitly justified by the ML/DL model itself. Representative instances in this family are: Attention Mechanisms rely upon contextual vector and attention mechanisms used to learn a conditional distribution over data inputs which provide an interpretation on the behavior of the weights of the operations of activation and deactivation, e.g. Single Modal Weighting [START_REF] Hendricks | Generating visual explanations[END_REF], Multimodal Interaction [START_REF] Anderson | Bottom-up and top-down attention for image captioning and visual question answering[END_REF]. Joint Training consists in introducing an additional task in the ML/DL model, asides the original one, in charge of providing direct or indirect explanations for the main task behavior, e.g. Text Explanation [START_REF] Liu | Towards explainable NLP: A generative explanation framework for text classification[END_REF], Explanation Association [START_REF] Iyer | Transparency and explanation in deep reinforcement learning neural networks[END_REF].

Being a qualitative feature, explainability in turn requires criteria to evaluate the quality of explanations. This presupposes a non-negligible intervention of humans in the assessment process. Some methods proposed to evaluate explanations can be found in [START_REF] Xie | Explainable deep learning: A field guide for the uninitiated[END_REF].

Conclusion and perspective

This paper presented the method used in Confiance.ai to tackle the issue of trustworthiness assessment, in the context of safety-critical AI-based systems. Trustworthiness is a complex notion, combining subjective concepts, heterogeneity of granularity in the attributes composing it, and non-commensurability of the different attributes. The approach consists in defining the different attributes constituting the notion of trustworthiness, an exploration of each attribute to determine related KPIs or assessment methods, and the definition of an aggregation methodology based on a MCDA approach. Some of such KPI examples were illustrated in data-driven AI context. The work envisions the creation of a methodological framework for the assessment of trustworthiness that leverages expert knowledge (for example in the definition of thresholds), a modeling of the environment of the application (e.g. influence of the Operational Design Domain on the selection of attributes), and usability in an engineering process (each atomic attribute is linked to a method or metric), covering other AI paradigm in order to go beyond ML.

Figure 1 :

 1 Figure 1: Evolution of the number of publications on Google Scholar between 1950 and 2019

Figure 2 :

 2 Figure 2: The unified approach based on MCDA

Figure 3 :

 3 Figure 3: A first mindmap to structure the problem of AI-based system trustworthiness assessment the human right to give the AI system decision-making authority or to revoke that authority at any time.From interaction's perspective, trustworthy AI should possess the properties of usability, explainability and interpretability. Specifically, AI-based systems should not cease operation at inappropriate times (e.g. at times when the lack of output could lead to safety risks), and these programs or systems should be easy to use for people with different backgrounds. Trustworthy AI solutions should allow explanation and analysis by humans to reduce potential risks and harms and empower human users. In addition, trustworthy AI should be transparent so people can better understand its mechanism.Ethics, strongly linked in Europe to the notion of fundamental rights, is notably put forward in the work of the AI HLEG (High-Level Expert Group on Artificial Intelligence) of the European Commission[START_REF] Hleg | Assessment list for trustworthy artificial intelligence (altai) for self-assessment, High Level Expert Group on Artificial Intelligence[END_REF]. A system must, for example, be law-abiding, fair, accountable, environmentally friendly and compliant with the user privacy. Specifically, AI systems should operate in accordance with all relevant laws and regulations, as well as with the ethical principles of human society.As some notions (e.g. explainability) concern several dimensions (ethics vs. interaction), Confiance.ai program made an arbitrary choice to be consistent with the methodology. Finally, the attributes for trusted ecosystem intermediaries focus on the relationships to thirdparties, in particular quality assurance, audit and certification activities. All these properties apply to the AI-based component and the system that embeds AI, but they also apply to the quality of the data used for training connectionist AI and/or to the quality of knowledge

  Figure of Merit -represented by the set of its possible values 𝑋𝑖. The alternatives are characterized by a value on each attribute and can be fully described by elements of 𝑋 = 𝑋1 × ... × 𝑋𝑛. An alternative 𝑥 can thus be represented by a vector (𝑥1, ..., 𝑥𝑛) ∈ 𝑋.

Figure 4 :

 4 Figure 4: Radar chart is useful for comparing two systems which embed different AI approaches.

Figure 5 :

 5 Figure 5: Performance metrics for classification problems

Figure 6 :

 6 Figure 6: Some data quality characteristics defined in ISO/IEC 25012:2008

  robustness is satisfied a single data input 𝑥 ∈ 𝐷 of a model 𝑀 and a given perturbation 𝑥 ′ within a neighborhood 𝛿 iff 𝑀 (𝑥) is identical to 𝑀 (𝑥 ′ ), in other words: ∀𝑥 ′ , 𝑑(𝑥, 𝑥 ′ ) ≤ 𝛿 ⇒ 𝑀 (𝑥) = 𝑀 (𝑥 ′ ) Global robustness is satisfied by the set of data 𝐷 of a model 𝑀 , considering possible 𝛿 perturbations 𝑥 ′ for all inputs 𝑥 ∈ 𝐷, and exhibiting smooth convergence of 𝑀 (𝑥 ′ ) towards 𝑀 (𝑥) during classification, in other words: ∀𝑥, 𝑥 ′ ∈ 𝐷, 𝑑(𝑥, 𝑥 ′

Acknowledgment

This work has been supported by the French government under the "France 2030" program, as part of the SystemX Technological Research Institute within the Confiance.ai Program (www.confiance.ai).