Foundation
Foundation
At Safe and Sound, we are committed to bridging the gap between robotics development and policy, ensuring that science-driven evidence informs regulatory frameworks. Our vision is to create a safer, more inclusive, and forward-thinking approach to robotics governance.
However, the need for this approach did not emerge in isolation. The rapid evolution of service robots has outpaced existing legal frameworks, leading to critical gaps in safety and regulatory oversight. Recognizing this challenge, we have worked for years to establish the necessary groundwork—exploring the intersection of robotics, law, ethics, and governance.
The Safe and Sound project builds upon a series of key studies, interdisciplinary collaborations, and past research grants that have explored these topics before.
Fosch-Villaronga, E. and Heldeweg, M. A., (2018) "Regulation, I Presume?" Said the Robot. Towards an Iterative Regulatory Process for Robot Governance. Computer Law and Security Review, 34(6), 1258-1277.
This article envisions an iterative regulatory process for robot governance. In the article, we argue that what lacks in robot governance is actually a backstep mechanism that can coordinate and align robot and regulatory developers. In order to solve that problem, we present a theoretical model that represents a step forward in the coordination and alignment of robot and regulatory development.
Our work builds on previous literature, and explores modes of alignment and iteration towards greater closeness in the nexus between research and development (R&D) and regulatory appraisal and channeling of robotics’ development. To illustrate practical challenges and solutions, we explore different examples of (related) types of communication processes between robot developers and regulatory bodies. These examples help illuminate the lack of formalization of the policymaking process, and the loss of time and resources that the waste of knowledge generated for future robot governance instruments implies.
We argue that initiatives that fail to formalize the communication process between different actors and that propose the mere creation of coordinating agencies risk being seriously ineffective. We propose an iterative regulatory process for robot governance, which combines the use of an ex ante robot impact assessment for legal/ethical appraisal, and evaluation settings as data generators, and an ex post legislative evaluation instrument that eases the revision, modification and update of the normative instrument. In all, the model breathes the concept of creating dynamic evidence-based policies that can serve as temporary benchmark for future and/or new uses or robot developments.
Our contribution seeks to provide a thoughtful proposal that avoids the current mismatch between existing governmental approaches and what is needed for effective ethical/legal oversight, in the hope that this will inform the policy debate and set the scene for further research.
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.
Calleja, C., Drukarch, H., and Fosch-Villaronga, E. (2022). Harnessing robot experimentation to optimize the regulatory framing of emerging robot technologies. Data & Policy, Cambridge University Press, 1-15.
From exoskeletons to lightweight robotic suits, wearable robots are changing dynamically and rapidly, challenging the timeliness of laws and regulatory standards that were not prepared for robots that would help wheelchair users walk again. In this context, equipping regulators with technical knowledge on technologies could solve information asymmetries among developers and policymakers and avoid the problem of regulatory disconnection.
This article introduces pushing robot development for lawmaking (PROPELLING), an financial support to third parties from the Horizon 2020 EUROBENCH project that explores how robot testing facilities could generate policy-relevant knowledge and support optimized regulations for robot technologies. With ISO 13482:2014 as a case study, PROPELLING investigates how robot testbeds could be used as data generators to improve the regulation for lower-limb exoskeletons. Specifically, the article discusses how robot testbeds could help regulators tackle hazards like fear of falling, instability in collisions, or define the safe scenarios for avoiding any adverse consequences generated by abrupt protective stops. The article’s central point is that testbeds offer a promising setting to bring policymakers closer to research and development to make policies more attuned to societal needs.
In this way, these approximations can be harnessed to unravel an optimal regulatory framework for emerging technologies, such as robots and artificial intelligence, based on science and evidence.