Application of the General Data Protection Regulation on Household Social Robots
This doctoral dissertation examines the application of the European Union General Data Protection Regulation (GDPR) to personal household social robots (HSRs). It investigates the practical, legal, and technical challenges of applying data protection rules—such as consent, purpose limitation, transparency, and data controllership—to autonomous systems that process personal data through machine learning. Through legal analysis, future scenarios, and expert interviews across four EU member states, the study evaluates accountability gaps and proposes recommendations for lawmakers, developers, and data protection authorities.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This dissertation should be read by legal scholars, policymakers, and data protection regulators seeking to understand the regulatory challenges of applying privacy law to embodied artificial intelligence.
What the document contains
The dissertation covers the legal and practical applicability of the GDPR to household social robots, focusing on consent, profiling, automated decision-making, transparency, purpose limitation, joint controllership, and household exemptions.
The text does not provide specific technical source code analysis, commercial product pricing models, or binding legislative drafts for new AI regulations.
- The GDPR applies to personal household social robots, but its broad principles create ambiguity regarding autonomous data processing.
- Obtaining valid, informed, and unambiguous consent is practically difficult in ubiquitous human-robot interactions due to unpredictable data collection and technical complexity.
- The traditional household exemption may not fully cover users of advanced social robots if data processing impacts third parties or extends beyond purely domestic spheres.
- Expert interviews across Finland, Hungary, Italy, and the Netherlands reveal divergent legal interpretations and enforcement approaches regarding AI data protection compliance.
- Proactive regulatory tools such as compulsory user training, certification systems, and algorithm explainability are necessary to bridge accountability gaps.
1. Introduction 2. Right to Data Protection 3. Definition of Artificial Intelligence and Personal Social Robots 4. AI and Robotics in the EU 5. HSR and Data Protection: Problem Statement 6. Analysis of the Research Questions and Expert Opinions 7. Conclusions and Recommendations
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Enforcement
4/5Consequences and compliance mechanisms with real force
NSAs have the competence to start or take a part in legal proceedings before judicial authorities. The NSAs are now in a stronger position within the country they have jurisdiction
II. Right to Data Protection
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Article 35 of the GDPR introduces a new tool for data controllers to self-check and to prove their compliance with the GDPR based on proactive measures
II. Right to Data Protection
screening score · 2026-10-23