LIAISON

FSTP from the H2020 COVR Project

Liaising robot development & policymaking

This project aims to link robot development and policymaking to reduce the complexity in robot legal compliance in the context of COVR.

Background

COVR stands for "being safe around collaborative and versatile robots in shared spaces" and is an H2020 COVR Project is a European project that aims to reduce the complexity in safety certifying cobots significantly. In this respect, the project has developed the COVR Toolkit. This online tool guides developers on their legal compliance process, from helping them find relevant standards/directives/protocols to guide them on how to do a risk assessment.

Since 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 very helpful. However, new robot applications may not fit into existing robot categories, and legislation (private and public policy making) might be outdated and include confusing types. In the context of H2020 COVR, LIAISON investigates to what extent we could use compliance tools as data generators for policymakers to unravel an optimal regulatory framing for existing and emerging robot technologies. The goal is to link robot development and policymaking to reduce the complexity in robot legal compliance.

The project LIAISON

In this respect, LIAISON will conceive a practical way to extract compliance and technical knowledge from compliance tools that help developers comply with the legislation, such as the COVR toolkit. The goal is to direct this knowledge to policymakers to help them work out an adequate regulatory framing (including change, revise, or reinterpret) that reflect the existing and emerging robot landscape. 

LIAISON aligns with the overall H2020 COVR goal to reduce complexity in safety certifying robots by providing policymakers with the necessary knowledge about legal inconsistencies, new categories, or new safety requirements (including psychological) to update existing frameworks. For more information read this journal publication and in this book chapter.

The model

As depicted in Figure 1, LIAISON conceives an effective way to extract compliance and technical knowledge from the COVR Toolkit and direct these data to policy and standard makers to unravel an optimal regulatory framing, including decisions to change, revise, or reinterpret existing regulatory frameworks for existing and emerging robot technologies. More practically, LIAISON’s objective is to clarify what regulatory actions policy and standard makers should take to provide compliance guidance, explain unclear concepts or uncertain applicability domains to improve legal certainty, and inform future regulatory developments for robot technology use and development at the European, National, Regional, or Municipal level (Fosch-Villaronga and Drukarch 2020). This is achieved through the LIAISON model, depicted in Figure 1 below. In general terms, the LIAISON model puts forward a threefold model through which by (a) interacting with compliance tools (in this case in interaction with the COVR Toolkit, but it could also be in interaction with the Assessment List of Trustworthy AI (ALTAI) model developed by the EC)7; (b) extracting knowledge from them in partnership with developers and other actors; and (c) sharing this knowledge with engaged regulators to support regulatory action, we can govern robot technology more effectively (Fosch-Villaronga and Heldeweg 2018, Fosch-Villaronga and Heldeweg, 2019). 

Do you want to read more? Read the full article following this link.

Deliverables

Milestone 1 

LIAISON _ MS1 _ D1.1. Report safety standards’ uncovered challenges.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), COVR LIAISON – D1.1 Report on safety standards’ uncovered challenges LIAISON nr. MS1. Leiden: eLaw / Leiden University. 

LIAISON _ MS1 _ D1.2. _ Report usefulness of LIAISON.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON – D1.2 Report on usefulness of LIAISON. eLaw Center for Law and Digital Technologies at Leiden University.  

LIAISON _ MS1 _ D1.3. Report exploratory meetings with relevant policymakers.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON – D1.3 Report on exploratory meetings with relevant policy/standard makers eLaw Center for Law and Digital Technologies at Leiden University. 

Milestone 2 

LIAISON_MS2_D2.1 Recommendations for the COVR Toolkit update_V2.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON - D2.1 Recommendations for the COVR Toolkit update, eLaw Center for Law and Digital Technologies at Leiden University.

LIAISON_MS2_D2.2. Lecture on the 'future of law'_V2 .pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON, D2.2 | Lecture on the ‘future of law’. eLaw Center for Law and Digital Technologies at Leiden University.

LIAISON_MS2_D2.3. Academic publication(s) featuring the future of robot governance_V2.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON D2.3 | Academic publication featuring the future of robot governance. eLaw Center for Law and Digital Technologies at Leiden University.


LIAISON_MS2_D2.4. Two policy briefs for standard and policy makers_V2.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON - D2.4 | Policy brief for standard and policymakers (EU & NEN). eLaw Center for Law and Digital Technologies at Leiden University.

LIAISON_MS2_D2.5 LIAISON Lessons learned and evaluation report.pdf

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON - D2.5 | LIAISON Lessons learned and evaluation report. eLaw Center for Law and Digital Technologies at Leiden University.

LIAISON _ MS2 _ D2.6. Results and achievements M2.pptx

Fosch Villaronga E. & Drukarch H.G. (2021), H2020 COVR FSTP LIAISON - D2.6 |MS2 COVR presentation describing MS2 results and achievements. eLaw Center for Law and Digital Technologies at Leiden University.

Academic Outputs

LIAISON: Liaising robot development and policymaking to reduce the complexity in robot legal compliance


Drukarch, H.,  Calleja, C., and Fosch-Villaronga, E. (2022). LIAISON: Liaising robot development and policymaking to reduce the complexity in robot legal compliance. In: Pons, J. L. (2022) Interactive Robotics: Legal, Ethical, Social and Economic Aspects. Biosystems & Biorobotics, vol. 30, Springer., 212-219, https://doi.org/10.1007/978-3-031-04305-5_37
The relationship between robots and policy development is complex. Technology and regulation evolve, but not always simultaneously or in the same direction. At the same time, robot developers struggle to find suitable safeguards in existing norms applicable to them. This often results in disconnections between both worlds. New robots and applications may not fit into existing (robot) categories (a robotic garbage collector or a robotic wheelchair with a robotic arm with a feeding function). Also, regulations may be hard to follow for developers who are concerned about their particular case because legislation (private and public policy making) may be outdated and with confusing types (such as ‘personal care robots’ not for medical purposes from ISO 13482:2014), and technology-neutral. Since legal responsiveness does not always follow as a consequent step in response to technology’s dramatic pace, we initiated the LIAISON Research Project. LIAISON follows the ideal that lawmaking ‘needs to become more proactive, dynamic, and responsive’ to achieve its desired policy goals and explores to what extent compliance tools could be used as data generators for robot policy purposes to reduce the complexity in emerging robot governance, and unravel an optimal regulatory framing for existing and emerging robot technologies.


An Iterative regulatory process for robot governance

Drukarch, H., Calleja, C., and Fosch-Villaronga, E. (2023). An iterative regulatory process for robot governance. Data & Policy, Cambridge University Press, 5:e8, 1-22 

There is an increasing gap between the policy cycle’s speed and that of technological and social change. This gap is becoming broader and more prominent in robotics, that is, movable machines that perform tasks either automatically or with a degree of autonomy. This is because current legislation was unprepared for machine learning and autonomous agents. As a result, the law often lags behind and does not adequately frame robot technologies. This state of affairs inevitably increases legal uncertainty. It is unclear what regulatory frameworks developers have to follow to comply, often resulting in technology that does not perform well in the wild, is unsafe, and can exacerbate biases and lead to discrimination. This paper explores these issues and considers the background, key findings, and lessons learned of the LIAISON project, which stands for “Liaising robot development and policymaking,” and aims to ideate an alignment model for robots’ legal appraisal channeling robot policy development from a hybrid top-down/bottom-up perspective to solve this mismatch. As such, LIAISON seeks to uncover to what extent compliance tools could be used as data generators for robot policy purposes to unravel an optimal regulatory framing for existing and emerging robot technologies. 

Contribution to a CEN Workshop Agreement

CWA 17835:2022 on Guidelines for the development and use of safety testing procedures in human-robot collaboration

As a result of his participation to LIAISON and the H2020 COVR, Dr. Eduard Fosch-Villaronga contributed to the CEN Workshop Agreement CWA 17835:2022 on Guidelines for the development and use of safety testing procedures in human-robot collaboration.

cwa17835_2022.pdf