Liability for robots II: an economic analysis
This academic paper proposes and analyzes a novel legal liability regime called manufacturer residual liability (MRL) for robot-related accidents. Under MRL, operators and victims remain liable for negligence while manufacturers bear residual liability for non-negligent accidents. The economic model demonstrates that MRL achieves second-best efficiency by incentivizing efficient human care, R&D investments in safer robots, and the adoption of safer technologies in the marketplace.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Legal scholars, economists, policymakers, and regulators seeking rigorous economic analysis of liability rules for automated systems and robotics.
What the document contains
The document provides a formal economic and legal analysis of a proposed manufacturer residual liability (MRL) rule for accidents caused by robots involving human intervention. It evaluates how this liability regime impacts care levels, R&D investments, product pricing, technology adoption, activity levels, and litigation evidence production.
The paper does not provide empirical or experimental testing of the proposed MRL rule, nor does it address fully autonomous humans-out-of-the-loop robots or risk aversion among operators.
- Standard tort rules and products liability fail to address the multi-dimensional incentive problems of robot-generated accidents.
- A manufacturer residual liability rule holds operators and victims liable for negligence while making manufacturers residually liable for non-negligent accidents.
- The MRL rule incentivizes optimal human care, efficient R&D safety investments, and the adoption of safer robots in the marketplace.
- While MRL can lead to excessive activity levels for operators and victims, market pricing mechanisms can transmit liability costs and restore optimal activity levels.
- Manufacturers have comparative advantages in gathering and preserving evidence of operator negligence, improving adjudication compared to traditional torts.
1. Introduction 2. Related literature 3. A theory of manufacturer residual liability 4. Effects of manufacturers' residual liability 5. Final remarks
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Human Oversight
2/5Humans remain in control of consequential decisions
Our analysis assumes the existence of a human operator who has some form of control over the robot – control which can be exercised in a negligent manner.
3.1 Terminology and scope
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Enforcement
4/5Consequences and compliance mechanisms with real force
Assigning the residual liability to the manufacturer incentivizes manufacturers to incorporate evidence technologies into their robots
4.3 Manufacturers' incentives to prove the negligence of operators and victims
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
requires an upfront investment to reduce the probability of accidents occurring, whose effectiveness does not diminish with increasing activity levels
2. Related literature
screening score · 2026-10-23