Record STMT-WFEB93BAdvisory

Challenges and Solutions to Criminal Liability for the Actions of Robots and AI

Report State lawGlobalin force

This academic paper explores whether existing criminal law tools are sufficient to address criminal liability for the actions of robots and AI without creating new legal constructs. The authors argue against introducing direct criminal liability or fictitious legal personhood for AI itself, concluding instead that traditional criminal law—applied through corporate criminal liability, detailed audit trails, and digital twins—is adequate for prosecuting human or corporate negligence. The study examines robot generations, autonomy, causation, and prevention measures to establish responsibility within current legal frameworks.

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

Who issued it

Brno University of TechnologyAcademic · CZ

Publisher

Legal forceAdvisory

Under whose authorityState law

Judged fromfull text

Who it governs

Researchers and academicsNational governmentRegulators

Legal scholars, lawmakers, and researchers interested in the intersection of criminal law, robotics, and artificial intelligence liability.

Sectors

Research and science · academic research

What the document contains

What's covered

The paper analyzes how traditional criminal law principles—such as intent, negligence, and causation—apply to the unlawful actions and malfunctions of robots and AI systems. It evaluates robot generations, software reliability, risk management, and evidentiary requirements including audit trails and digital twins.

What's missing

It does not provide binding legislative text, regulatory enforcement mechanisms, or specific statutory drafts, as it is an academic research study rather than a formal policy document.

Key takeaways

Traditional criminal law constructs are sufficient to address crimes involving robots and AI without needing to invent novel legal personhood for machines.
Criminal liability should focus on human actors and corporate entities, such as manufacturers, operators, and users, via negligence or corporate criminal liability.
Proving causation in third- and fourth-generation autonomous robots presents significant challenges due to self-learning and unpredictable internal states.
Detailed audit trails, black boxes, and digital twins are essential technical mechanisms to gather evidence and trace the rationale of an AI decision in criminal proceedings.

Outline

· 1. Introduction
· 2. The Term “Robot” and Categories of Robots
  - 2.1. Definition of robot
  - 2.2. Generations of robots
  - 2.3. Self-driving cars
· 3. Liability for the Robot’s Actions
  - 3.1. A new dimension of liability
  - 3.2. Tort liability in robotics
  - 3.3. Negligent fault in robotics
  - 3.4. Causality in robotics
  - 3.5. Robot software and tort liability
  - 3.6. Other factors affecting robots’ operation
  - 3.7. Legal persons as potential perpetrators of robotic crimes
· 4. Prevention and Evidence
  - 4.1. Prevention
  - 4.2. Evidence
  - 4.3. Digital twins
· 5. Results and Discussion
· 6. Conclusion

Issued23 September 2023

Length13,091 words

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

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-23

Human Dignity

0/5

Respect for the inherent worth of persons

screening score · 2026-10-23

Privacy

0/5

Protection of personal data and private life

screening score · 2026-10-23

Topics

Physical AI Governancesupporting

criminal_liability robotics audit_trail digital_twins negligence