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Liability theory and the issues in automated vehicle incidents

The attribution of liabilities in automated vehicle incidents is quite complex. Prof. Ching-Fu Lin discusses it in this article.

Tort law focuses on assigning responsibility and compensation when harm or injury is caused. Liability theory helps determine the level of blame and its associated compensation.

Four Main Types of Liabilities in Tort Law


Occurs when someone fails to act as a cautious person would in a similar situation. Four core elements to establish negligence: – Duty of care: Defendant had a legal duty towards the plaintiff. – Breach of duty: Defendant did not act reasonably. – Causation: Defendant’s breach directly caused plaintiff’s injury. – Damages: Plaintiff was harmed due to defendant’s actions.
Recklessness (Wanton Conducts)
Happens when someone knowingly disregards others’ safety, even when aware of significant risks. It’s a step above negligence as the person knows of the risks but acts anyway.
Intentional Misconduct
Intentional harm caused to another person. Actions are purposeful, not just careless or reckless. Examples: Assault, fraud, etc. Often results in more severe consequences than negligence or recklessness.
Strict Liability
Holds one responsible for damages, irrespective of fault or intent. Just proving harm and defendant’s role in the activity causing harm is enough. Often linked to very risky activities like using explosives or keeping dangerous animals.

Classical vs. Automated Vehicle Incidents:

Classical Car Incidents

  • Based on drivers’ actions or negligence. For example, if one driver speeds and hits another car, they’re solely at fault. However, if both drivers commit errors (e.g., one speeds and the other runs a red light), the blame may be shared. Concepts like “comparative negligence” might be used to distribute responsibility.

Automated Vehicle Incidents

  • Can involve multiple parties, from vehicle owners to system providers, manufacturers, and more. Causes for incidents can be diverse, such as infrastructure failures, cyberattacks, or system glitches. Determining liability can be more challenging due to the involvement of multiple stakeholders and evolving regulations.
© Ching-Fu Lin and NTHU, proofread by ChatGPT
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AI Ethics, Law, and Policy

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