Record STMT-WF8614ESoft law

An iterative regulatory process for robot governance

Report Institutional policyGlobaladopted

This research article explores the growing gap between the fast pace of technological and social change in robotics and the slower policy cycle. It examines the background, key findings, and lessons learned from the LIAISON project, which aims to ideate an alignment model for robots' legal appraisal. By proposing a hybrid top-down/bottom-up perspective, the study investigates how compliance tools can serve as data generators to optimize regulatory frameworks for existing and emerging robotic technologies.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Leiden UniversityAcademic · NL

Publisher

Legal forceSoft law

Under whose authorityInstitutional policy

Judged fromfull text

Who it governs

National governmentRegulatorsAI developers and providersResearchers and academics

This document is for policymakers, researchers, standard makers, and robot developers seeking to understand and improve the alignment between robot technology development and regulatory frameworks.

Sectors

Research and science · roboticsHealthcare · personal care and rehabilitation roboticsAgriculture and food · agricultural robotics

What the document contains

What's covered

The document addresses the legal, ethical, and regulatory challenges surrounding emerging robotic technologies across personal care, rehabilitation, and agricultural domains. It evaluates current safety standards, highlights information asymmetries between developers and policymakers, and proposes an iterative regulatory feedback model.

What's missing

The document does not provide binding statutory regulations, enforceable compliance frameworks, or a comprehensive code of conduct for commercial robot deployment.

Key takeaways

There is a growing gap between the fast pace of technological change in robotics and the slower speed of the policy cycle.
Compliance tools such as the COVR Toolkit can be leveraged as data generators to inform policymakers of regulatory gaps and inconsistencies.
The LIAISON project establishes an iterative regulatory model utilizing a hybrid top-down and bottom-up perspective for robot governance.
Current robot safety standards often lack clarity, fail to cover specific vulnerable user groups, and struggle to address emerging cyber-physical and psychological risks.
Effective robot governance requires broad interdisciplinary cooperation among developers, standard makers, regulators, and end-users.

Outline

· Abstract
· Policy Significance Statement
· 1. Introduction
· 2. Aligning Robot Development and Regulation: A New Model
  - 2.1. Difficulties in regulating emerging robotics
  - 2.2. LIAISON: Liaising robot development and policymaking
· 3. The Inner Workings of LIAISON: Methods and Knowledge Extraction Mechanisms
  - 3.1. Desktop research
  - 3.2. Feedback loops: Survey distribution
  - 3.3. Intensifying knowledge collection: Dedicated workshops in key venues
  - 3.4. Data collection and redirection
· 4. Key Findings, Lessons Learned, and Discussion from the LIAISON Project
  - 4.1. A mere website showing regulations is not enough help to robot developers comply
  - 4.2. An ecosystem encompassing all stakeholder interests is lacking
  - 4.3. Engaging with robot developers can generate policy-relevant knowledge
  - 4.4. A bridging the gap through interdisciplinary cooperation
  - 4.5. Lack of legal comprehension
· 5. Conclusion

Issued1 February 2023

Length13,306 words · 23 pages

FormatNot recorded

Scopeglobal

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Fairness

2/5

Non-discrimination and equitable treatment

is unsafe, and can exacerbate biases and lead to discrimination.

Abstract

screening score · 2026-10-25

Human Dignity

2/5

Respect for the inherent worth of persons

their continuous use could increase isolation, deception, and loss of dignity

1. Introduction

screening score · 2026-10-25

Human Oversight

2/5

Humans remain in control of consequential decisions

Such operators are not always present with the robot but nevertheless may remain legally responsible for the safety of operations and must, therefore, be able to place the machine in safe mode at all times.

4.3.3. Findings concerning agriculture robots

screening score · 2026-10-25

Privacy

2/5

Protection of personal data and private life

technology developers often times initially focus on creating a product and successfully bringing it to market, before they take into consideration the wider ethical and legal concerns—including, for instance, privacy and data protection

1. Introduction

screening score · 2026-10-25

Transparency

2/5

Openness about how AI systems work and are used

and they may call into existence discriminatory responses that are not always sufficiently transparent and may seriously affect society

1. Introduction

screening score · 2026-10-25

Enforcement

2/5

Consequences and compliance mechanisms with real force

This state of affairs inevitably increases legal uncertainty. It is unclear what regulatory frameworks developers have to follow to comply

Abstract

screening score · 2026-10-25

Impact Assessment

4/5

Required assessment of an AI system's risks/impacts before or during use

LIAISON departs from the idea that developers may identify legal inconsistencies among regulations or may call into existence new categories of devices that struggle to fit any of the categories

1. Introduction

screening score · 2026-10-25

Topics

Physical AI Governancesupporting

robot_governance evidence_based_policy policy_cycle regulatory_process robot_technology