Horizon 2020(H2020)779966Effective Protection of Fundamental Rights in a pluralist worl

Drukarch, H.G.

Fosch Villaronga, E.

Leiden University Scholary Publications

H2020 COVR FSTP LIAISON – D2.2 Lecture on the ‘future of law’Fosch Villaronga, E.; Drukarch, H.G.CitationFosch Villaronga, E., & Drukarch, H. G. (2021). H2020 COVR FSTP LIAISON – D2.2Lecture on the ‘future of law’. LIAISON. Leiden: eLaw / Leiden University. Retrieved fromhttps://hdl.handle.net/1887/3278376 Version: Publisher's VersionLicense: Leiden University Non-exclusive licenseDownloaded from: https://hdl.handle.net/1887/3278376 Note: To cite this publication please use the final published version (if applicable).LIAISOND2.2 Lecture on the 'future of law'Type: ReportAccess: PublicDate: August 2021 (M9)Author(s): Eduard Fosch-Villaronga (eLaw, Leiden University)Hadassah Drukarch  (eLaw, Leiden University)Revisions: 1Deliverable: D2.2Grant agreement no: 779966Date: 2021-08-31CONTENTS1. SUMMARY 32. INTRODUCTION 43. LIAISON 53.1. BACKGROUND 54. LECTURE ON THE ‘FUTURE OF LAW’ 74.1. LECTURE 74.4. TEACHING 75. REFERENCES 106. ANNEX 1121. SUMMARYWhile robots should be safe, robot regulatory frameworks do not always frame technologydevelopment accurately. LIAISON investigates to what extent compliance tools, in this case,the COVR toolkit, could be used as data generators for policy and standard makers tounravel an optimal regulatory framing for existing and emerging robot technologies andimprove robot technology overall safety and market entrance ease. As such, LIAISON alignswith the overall COVR goal to reduce complexity in safety certifying robots.New technologies sometimes question and challenge existing norms, breathing intoexistence the need for legal change. While the pace of technology dramatically accelerates,however, legal responsiveness does not always follow as a consequent step. As no formalcommunication process between robot developers and regulators from which policies couldlearn has been established yet, a stepback mechanism for robot governance as novel asthat introduced by LIAISON is yet to be introduced to all stakeholders involved, in particularrobot developers and policy/standard makers. To prove the feasibility and added value of thecreation of this link between robot developers and relevant regulators, for the LIAISONproject, we focus on three particular standards: ISO 13482:2014 on personal care robots,IEC 80601-2-78:2019 on rehabilitation robots, and EN ISO 18497:2018 on agriculturalmachinery and tractors. The policy and standard makers involved for this purpose representboth private standardisation organisations and the European Commission.This report presents the lecture on the ‘future of law’ provided as part of D2.2. of the FSTPLIAISON project, and the teaching activities based on this lecture.32. INTRODUCTIONCOVR stands for "being safe around collaborative and versatile robots in shared spaces'',and is a European H2020 Project which aims to reduce the complexity in safety certifyingcobots significantly. In this respect, the project has developed the COVR Toolkit, an onlinetool that guides developers in their legal compliance process, from helping them find relevanttechnical standards/directives/protocols to guide them on performing a risk assessment.Assessing risks through experimentation is essential to ensure robot safety and compliancewith existing norms. However, standards do not always frame technology developmentaccurately. LIAISON investigates to what extent compliance tools (tools that help complywith the legislation, such as the COVR toolkit) could be used as data generators for policyand standard makers to unravel an optimal regulatory framing (including change, revise, orreinterpret) for existing and emerging robot technologies. LIAISON is a crucial stepbackmechanism to help align robot and regulatory development and improve robot technology'soverall safety and market entrance ease. To prove the feasibility and added value of thecreation of this link between robot developers and relevant regulators, for the LIAISONproject, we focus on three particular standards: ISO 13482:2014 on personal care robots,IEC 80601-2-78:2019 on rehabilitation robots, and EN ISO 18497:2018 on agriculturalmachinery and tractors. The policy and standard makers involved for this purpose representboth private standardisation organisations and the European Commission. As such,LIAISON aligns with the overall COVR goal to reduce complexity in safety certifying robotsby providing policy and standard makers with the necessary knowledge about legalinconsistencies, new categories, or new safety requirements (including psychological) toupdate existing frameworks where necessary and to ensure that the next generation ofrobots is 'safe' to the full extent of the word (see figure 1 in the annex). In this way, LIAISONcontributes to the COVR mission by adding a link to public and private regulators tocomplete the cobot value chain.This report presents the lecture on the ‘future of law’ provided as part of D2.2. of the FSTPLIAISON project, and the teaching activities based on this lecture.43. LIAISON3.1. BACKGROUND“The art of progress is to preserve order amid change, and to preserve change amid order” –Alfred North Whitehead.Robot technology is one of the many technologies that challenge the regulatory framework invarious ways, including ethics and security for responsible innovation, privacy, andresponsibility allocation. As products, robots widely differ in embodiment, capabilities,context of use, intended target users, and many regulations may already apply to them.Having tools such as the COVR Toolkit can be of help. However, new applications may not fitinto existing (robot) categories, legislation might be outdated and confusing categories, andtechnology-neutral regulations may be hard to follow for developers concerned about theirparticular case. A recent open consultation launched by the European Commission, forinstance, acknowledges that current European Harmonized Standards do not cover areassuch as automated vehicles, additive manufacturing, collaborative robots/systems, or robotsoutside the industrial environment, among others (Spiliopoulou-Kaparia, 2017). In light of allthe issues this technology arises, part of the literature accentuates the need for an issuemanager. Marchant and Wallach (2015) proposed the creation of "Governance CoordinatingCommittees (GCC)" for the governance of emerging technologies like AI.Furthermore, the European Parliament proposed creating a European Agency for Roboticsand Artificial Intelligence early in 2017, and Schatz put forward the result of an emergingtechnology policy lab within the US general services administration in 2018. However, whatlacks in robot governance is a backstep mechanism that can coordinate and align robot andregulatory development (Fosch-Villaronga & Heldeweg, 2018). Overlooked in the latestreview of "the grand challenges of science robotics," this challenge has already been raisedin the literature, albeit only more recently (Yang et al. 2018), and relates to the idea of howpolicies can frame the rapid development of robotics. LIAISON contributes to theseapproaches by proposing the modus operandi of issue managers, if they were ever to exist,and revolves around the following main research question:Could the use of compliance tools, such as the COVR Toolkit, as data generators for robotpolicy purposes reduce emerging robot governance complexity?LIAISON envisions an iterative regulatory process for robot governance, a theoretical modelthat represents a practical step forward in the coordination and alignment of robot andregulatory development, called the Iterative Learning Governance Process (ILGP). Thisresearch project conceives an effective way to extract compliance and technical knowledgefrom compliance tools (tools that help comply with the legislation such as the COVR toolkit)and direct it to policy and standard makers to unravel an optimal regulatory framing(including change, revise, or reinterpret) for existing and emerging robot technologies. Thisprocess will bring clarity to what regulatory actions policy and standard makers have to taketo provide compliance guidance, explain unclear concepts or uncertain applicability domainsto improve legal certainty, and inform future regulatory developments for robot technologyuse and development at the European, National, Regional, or Municipal level. As such,5LIAISON is a crucial stepback mechanism to help align robot and regulatory developmentand improve robot technology's overall safety and ease of market entrance.The primary outcome of the LIAISON Research Project will be the design concept for liaisingrobot development and policymaking to increase overall robot safety. This design conceptwill further develop the Iterative Regulatory Process for Robot Governance, which wasideated as a theoretical model that links technology impact assessments to legislativeex-post evaluations via shared data repositories intending to create evidence-based policiesthat can serve as temporary benchmark for future and new uses or robot developments(Fosch-Villaronga & Heldeweg, 2018, 2019). Part of the 'technical challenge' is to put such atheoretical model into practice and in the context of the COVR project. Explained further infigure 2 in the Annex (Fosch-Villaronga & Heldeweg, 2018), such iterative regulatory processfor robot governance stresses that in the light of a new robot development or use, and afterassessing all the impacts (and incorporating the findings into the robot itself), it is essential tocompile all the Regulation-to-Technology uncovered barriers and constraints that do notallow the roboticists to proceed with their creation. Having collected those constraints in aTechnology-to-Regulation manner, the regulator can act thereupon supported by theaccountability tool's information, in this case, the COVR Toolkit.The expected project results will complement the existing knowledge on the 'ethical, legal,and societal (ELS)' aspects of robotics by providing clarity on how to address pressing butstill uncovered safety challenges raised by robots, and represent a practical, valuable tool toadvance social goals in a robotized workplace. Overall, advances in safety robot legaloversight will provide a solid basis for designing safer robots, safeguarding users' rights, andimproving the overall safety and quality of efficiency delivered by robots.64. LECTURE ON THE ‘FUTURE OF LAW’4.1. LECTUREPRESENTATIONTITLE LINKLecture on the 'future of law' CLICK HERELeiden University is a research-driven educational setting. The Lecture ‘future of law’ wasprepared based on the main findings of the FSTP LIAISON from the H2020 COVR Project.The lecture comprises an introduction to different regulatory models for robot technologies(from public policymaking and private setting) and raises the question of whether theseexisting mechanisms suffice to capture the highly volatile robot environment. Students areconfronted with the idea that robot technology pushes the boundaries of science and existingcategories and are asked to reason about what to do when there is a new category notpreviously foreseen in the law. Finally, after some interactive discussion, the lecture presentsthe LIAISON model as an alternative to existing mechanisms for robot governance.4.4. TEACHINGThe lecture on the ‘future of law’ was provided as part of the educational programs providedat Leiden Law School and at education institutions abroad as part of an invited lecture seriesas depicted in the table below.TEACHINGCOURSE DATES DESCRIPTIONLaw andartificialintelligence,Leiden LawSchool, TheNetherlands.09/02/2021 -08/06/2021In this course, we first map the latest advances in AI,including predictive policy, face recognition, personalizeddiagnosis, early disease detection drug discovery,algorithmic decision-making used in courts, andgovernment. We then identify what the main regulatoryinitiatives revolving around AI technologies in the EuropeanUnion are, including the Ethics Guidelines from theEuropean High-Level Expert Group on ArtificialIntelligence.During the course, we will focus on the benefits but alsothe particular challenges revolving around the deploymentof AI, including, transparency and explanation (followingthe European General Data Protection Regulation);potential discrimination scenarios and exacerbation ofexisting biases; the construction of responsibility in highlyautomated environments; and the blurring ofwell-established concepts such as safety, in the context of7AI and interconnected products. We will explore solutionsand also learn how to assess the risk posed by thesetechnologies via the use of impact assessments. We closethe course reflecting on the long-term consequences of AI,the added value this brings to society, and how the lawshould balance innovation and user rights protection in thecontext of AI.Robot Law,LeidenUniversity,TheNetherlands03/02/2021 -24/03/2021The integration of artificial intelligence (AI) technologies insociety is accelerating. AI-based systems can besoftware-based, acting in the virtual world mainly withsome physical support, such as voice assistants like Alexaor Google Home, image analysis software used fordiagnoses, recommendation systems for mortgages, crime,or patient management, search engines, or speech andface recognition systems. AI can also be embedded inmore complex hardware devices that operate in theenvironment, such as advanced robots, self-driving cars,drones, or Internet of Things applicationsThe High-Level Expert Group on Artificial Intelligenceappointed by the European Commission defined AI as'systems that display intelligent behavior by analyzing theirenvironment and taking actions – with some degree ofautonomy – to achieve specific goals.' These technologiesprocess vast amounts of data, can learn from experienceand self-improve their performance, which challenges theapplicability of existing regulations that were not designedfor progressive and adaptive AI. Since the automatedprocessing of data that will evaluate, analyze, and predictoutcomes that may affect the privacy, safety, or dignity ofindividuals, there is a growing interest in understandingwhat are the legal and regulatory implications of the use ofAI in society.RoboticGovernance(MykolasRomerisUniversitetasVilnius,Lithuania)April 2021 In this course, we first map the latest advances in AI,including predictive policy, face recognition, personalizeddiagnosis, early disease detection drug discovery,algorithmic decision-making used in courts, andgovernment. We then identify what the main regulatoryinitiatives revolving around AI technologies in the EuropeanUnion are, including the Ethics Guidelines from theEuropean High-Level Expert Group on ArtificialIntelligence.8During the course, we will focus on the benefits but alsothe particular challenges revolving around the deploymentof AI, including, transparency and explanation (followingthe European General Data Protection Regulation);potential discrimination scenarios and exacerbation ofexisting biases; the construction of responsibility in highlyautomated environments; and the blurring ofwell-established concepts such as safety, in the context ofAI and interconnected products. We will explore solutionsand also learn how to assess the risk posed by thesetechnologies via the use of impact assessments. We closethe course reflecting on the long-term consequences of AI,the added value this brings to society, and how the lawshould balance innovation and user rights protection in thecontext of AI.95. REFERENCESA Bill To authorize an emerging technology policy lab within the General ServicesAdministration, and for other purposes, S. 3502, 115th Cong. (2018).https://www.congress.gov/115/bills/s3502/BILLS-115s3502is.pdfEuropean Parliament resolution of 16 February 2017 with recommendations to theCommission on Civil Law Rules on Robotics (2015/2103(INL)).Fosch-Villaronga, E. (2019) Robots, Healthcare and the Law: Regulating Automation inPersonal Care. Routledge.Fosch-Villaronga, E., & Heldeweg, M. (2018). “Regulation, I presume?” said therobot–Towards an iterative regulatory process for robot governance. Computer Law &Security Review, 34(6), 1258-1277.Marchant, G.E., Allenby, B.R. and Herkert, J.R. eds., 2011. The growing gap betweenemerging technologies and legal-ethical oversight: The pacing problem (Vol. 7). SpringerScience & Business Media.Marchant, G.E. and Wallach, W., 2015. Coordinating technology governance. Issues inScience and Technology, 31(4), p. 43.Salvini, P., Laschi, C., & Dario, P. (2011). Do service robots need a driving license?. IEEErobotics & automation magazine, 18(2), 12-13.Spiliopoulou-Kaparia M. The evaluation of Directive 85/374/EEC on liability for defectiveproducts and Directive 2006/42/EC on machinery. Proceedings of the European StakeholderForum – Workshop on Regulatory challenges for a digitizing industry. Essen, 2017.Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N.,Kumar, V., McNutt, M., Merrifield, R. and Nelson, B.J., 2018. The grand challenges ofScience Robotics. Science Robotics, 3(14), p.eaar7650.106. ANNEXFigure 1: LIAISON Research Project mechanism11Figure 2: Preliminary iterative process for robot governance1 (Fosch-Villaronga &Heldeweg, 2018).1 As regards the meaning of arrows: #1. signifies that upon the initiative to develop a new robot (use) the ROBIA processcommences; #2 and #2a are about information about existing law/legal space being fed into the ROBIA fit to regulationprocess; #3 outcomes of ROBIA are reported to initiators to decide if and if so, how the development process can be continued;#4 and #5 concern reporting the decision and making information available to the SDR system; #6 is about how (changes in)information in SDR are a source of information to the ROBIA process – as shared learning; #7 is about information aboutexisting law with relevance to robotics is also part of the shared date in SDR (#2 is about specific legal information to a specificROBIA procedure; #7 about the general updating of legal info in SDR); #8 expresses that upon R2T events a process aboutpossible legal adjustments is started; #9 and #10 when it is decided (ex officio/ad petitionem) that some legal change may becalled for, a (basic) proposal is formulated whereupon the REGIA procedure is initiated; #11 and #12 show that outcomes of theREGIA procedure are reported back and feed into the decision on legal change; #13 Information in the report is also fed intoSDR to update regulatory information; #14 REGIA report can feed ROBIA without passing via the Existing law> box, as theREGIA report will say something about pros and cons of possible legal change, but should that change follow, then this willcommunicate via the <New law> box; #15 signifies adjustments in the law; #16 expresses that new law changes and becomespart of existing law.)12

H2020 COVR FSTP LIAISON – D2.2 Lecture on the ‘future of law’

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H2020 COVR FSTP LIAISON – D2.2 Lecture on the ‘future of law’

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