As artificial intelligence (AI) systems grow more advanced, their ability to make decisions with significant real-world consequences raises an increasingly urgent question: can AI be held legally liable for its actions?

From autonomous vehicles and financial algorithms to diagnostic systems in healthcare, AI increasingly acts as a decision-maker with real impact. When something goes wrong, who is responsible? Can an algorithm be sued? Should its developers or users bear the blame? These questions are shaping the future of accountability in law.

The Rise of Autonomous Decision-Making

In fields like healthcare and finance, AI systems are already making decisions that carry substantial risk and influence:

  • In healthcare, algorithms are used to interpret medical scans, suggest treatments, and predict patient outcomes.
  • In finance, AI-driven trading systems manage large-scale investments, while credit scoring models affect who qualifies for loans and insurance.

When an AI system causes harm, such as a misdiagnosis or a discriminatory decision, tracing the source of the problem becomes complex. This raises a key issue: who can and should be held legally accountable?

Legal responsibility typically depends on human intent or negligence. This model assumes that the actor is a person capable of making choices and understanding consequences. Since AI lacks consciousness, intent, and moral judgment, traditional legal categories do not apply easily.

This creates several complications:

  • Product liability could apply to those who create or distribute AI, but questions arise when systems evolve independently after deployment.
  • Professional negligence may target users of AI, such as doctors or financial advisors, but this risks punishing them for relying on tools that have been presented as highly reliable.

The Problem of Opacity

Many AI systems, particularly those using deep learning, are difficult to interpret. Even the developers may struggle to explain exactly how the system arrived at a specific decision. This lack of transparency makes it difficult to determine fault and apply legal doctrines like causation and duty of care.

As AI becomes more complex and independent in its functioning, assigning blame becomes more difficult under current legal structures.

Several legal and policy approaches have been proposed to address this accountability gap:

Some suggest creating a form of legal personhood for AI systems, similar to that granted to corporations. This would allow AI to be held directly responsible in legal proceedings. However, this idea remains controversial due to ethical and philosophical concerns, especially since AI lacks awareness or moral reasoning.

2. Strict Liability and Compensation Schemes

An alternative is to adopt a strict liability model. In this approach, those who deploy AI would be held liable for any harm it causes, regardless of fault. Another option is a no-fault compensation system, where victims are compensated without needing to prove negligence. This model could be similar to existing frameworks used for vaccine injuries.

3. Mandatory Insurance

A more practical solution involves requiring AI operators or developers to carry insurance. This would ensure that compensation is available if the system causes harm, similar to auto insurance for drivers of vehicles.

4. Regulatory Oversight

Governments and international bodies can play a role by setting clear rules for high-risk AI applications. The proposed EU AI Act is one example, introducing requirements for transparency, safety, and accountability. Such regulation can help prevent harm and clarify responsibility before issues arise.

Tailoring Solutions by Sector

Because AI operates differently in various sectors, a uniform legal approach may not be effective. Sector-specific frameworks may offer better protection and clarity.

  • In healthcare, AI might be treated as a medical device, subject to clinical trials and safety testing, with legal responsibility aligned with existing medical standards.
  • In finance, regulatory bodies could require regular audits of algorithms to detect bias and ensure fairness.

Each industry faces unique risks, and legal frameworks should reflect those differences.

Moving Toward Accountable AI

The idea of holding AI liable may seem far-fetched, but the need for accountability is real and immediate. As AI systems continue to shape decisions in law, health, finance, and more, the legal system must adapt to ensure fairness and justice.

While AI itself may not be capable of legal responsibility, those who design, deploy, and profit from it can and should be held accountable. Through a combination of regulation, insurance, and legal innovation, society can balance technological progress with the protection of rights and public safety.

Author's Note:
This article is part of an ongoing discussion about the intersection of law and technology. Interested in contributing your voice? Join SCL Student Bytes and become part of the conversation shaping the future of legal innovation.