Abstract:Current legislation may apply to new developments. Even so, these developments may raise new challenges that call into question the applicability of this legislation. This paper explains what happens at this moment for new robot technologies. We argue that there is no formal communication process between robot developers and regulators from which policies could learn. To bridge this gap, we propose a model that links technology impact assessments to legislative ex-post evaluations via shared data repositories.
“…This is achieved through the LIAISON model, which is 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 in a more effective way (Fosch-Villaronga and Heldeweg, 2018, 2019). Figure 1. LIAISON model for an iterative regulatory process for robot governance.…”
Section: The Inner Workings Of Liaison: Methods and Knowledge Extract...mentioning
confidence: 99%
“…An illustrative example of this can be found in the domain of healthcare robotics. From exoskeletons to surgery robots and therapeutic robots, healthcare robots challenge the timeliness of laws and regulatory standards that were unprepared for robots that, for instance, would help wheelchair users walk again (Tucker et al, 2015), perform surgeries autonomously (Shademan et al, 2016), or support children under autism spectrum disease in learning emotions (Scassellati et al, 2012; Fosch-Villaronga and Heldeweg, 2019). Furthermore, current legal frameworks tend to overfocus on physical safety but fail to account for other essential aspects like security, privacy, discrimination, psychological aspects, and diversity, which play a crucial role in robot safety.…”
Section: Aligning Robot Development and Regulation: A New Modelmentioning
confidence: 99%
“…New robots may not fit into existing (robot) categories, legislation might be outdated and include confusing categories, and technology-neutral regulations may be hard to follow for developers concerned about their particular case (Fosch-Villaronga, 2016; European Parliament, 2017a). The regulatory challenges arising from the novelty of the technology is a problem often disregarded among scientists (Yang et al, 2018), although it may have ulterior consequences for safety, security, and dignity (Fosch-Villaronga, 2019).…”
Section: Aligning Robot Development and Regulation: A New Modelmentioning
confidence: 99%
“…It is against this background that LIAISON has put forward the idea that this currently uncaptured knowledge could be formalized and serve as data to improve regulation. In other words, following the ideal that lawmaking "needs to become more proactive, dynamic, and responsive" (Fenwick et al, 2016), LIAISON attempts to formalize a communication process between robot developers and regulators from which policies could learn, thereby channeling robot policy development from a bottom-up perspective (Fosch-Villaronga andHeldeweg, 2018, 2019). To test its model, LIAISON focuses on personal care robots (ISO 13482:2014), rehabilitation robots (IEC 80601-2-78-2019), and agricultural robots (ISO 18497:2018).…”
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.
“…This is achieved through the LIAISON model, which is 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 in a more effective way (Fosch-Villaronga and Heldeweg, 2018, 2019). Figure 1. LIAISON model for an iterative regulatory process for robot governance.…”
Section: The Inner Workings Of Liaison: Methods and Knowledge Extract...mentioning
confidence: 99%
“…An illustrative example of this can be found in the domain of healthcare robotics. From exoskeletons to surgery robots and therapeutic robots, healthcare robots challenge the timeliness of laws and regulatory standards that were unprepared for robots that, for instance, would help wheelchair users walk again (Tucker et al, 2015), perform surgeries autonomously (Shademan et al, 2016), or support children under autism spectrum disease in learning emotions (Scassellati et al, 2012; Fosch-Villaronga and Heldeweg, 2019). Furthermore, current legal frameworks tend to overfocus on physical safety but fail to account for other essential aspects like security, privacy, discrimination, psychological aspects, and diversity, which play a crucial role in robot safety.…”
Section: Aligning Robot Development and Regulation: A New Modelmentioning
confidence: 99%
“…New robots may not fit into existing (robot) categories, legislation might be outdated and include confusing categories, and technology-neutral regulations may be hard to follow for developers concerned about their particular case (Fosch-Villaronga, 2016; European Parliament, 2017a). The regulatory challenges arising from the novelty of the technology is a problem often disregarded among scientists (Yang et al, 2018), although it may have ulterior consequences for safety, security, and dignity (Fosch-Villaronga, 2019).…”
Section: Aligning Robot Development and Regulation: A New Modelmentioning
confidence: 99%
“…It is against this background that LIAISON has put forward the idea that this currently uncaptured knowledge could be formalized and serve as data to improve regulation. In other words, following the ideal that lawmaking "needs to become more proactive, dynamic, and responsive" (Fenwick et al, 2016), LIAISON attempts to formalize a communication process between robot developers and regulators from which policies could learn, thereby channeling robot policy development from a bottom-up perspective (Fosch-Villaronga andHeldeweg, 2018, 2019). To test its model, LIAISON focuses on personal care robots (ISO 13482:2014), rehabilitation robots (IEC 80601-2-78-2019), and agricultural robots (ISO 18497:2018).…”
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.
“…Social Motivation Theory (SMT: Chevallier et al, 2012 ) highlights that ASD students usually prefer nonhuman and mechanical stimuli rather than seeking out or maintaining relationships with human partners ( Fosch-Villaronga and Heldeweg, 2018 ; Tavakoli, Carriere, and Torabi, 202; Burns et al, 2022 ). Social interaction challenges for ASD students stem from abnormal processing of social rewards, leading to decreased attention towards social cues early on.…”
Section: Social Robotics and Autism: A Review Of Literaturementioning
Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student–teacher co-working with social robot–social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students’ learning requirements through the use of social robots and preparing them for future learning and workforce environments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
