Generative AI & Law: Adapting Tort Law to Technology

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The rapid advancement of generative Artificial Intelligence (AI) technologies has revolutionized various sectors, from healthcare to finance, creating new opportunities and challenges. As AI systems become more integrated into daily life, the potential for these technologies to cause harm—whether through faulty algorithms, biased data, or unintended consequences—raises important legal questions. Applying established legal doctrines like tort law offers a practical pathway to address liabilities and harms caused by AI systems without the immediate need for entirely new legislation. Tort law, which deals with civil wrongs and damages, can be adapted to hold parties accountable for negligence, defects, and other harms associated with AI.

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Product Liability and Software

A central challenge in applying tort law to AI is determining whether software—and by extension, AI algorithms—should be considered a "product" under product liability law. Traditionally, product liability applies to tangible goods, but the intangible nature of software complicates this classification. As software becomes integral to physical products such as medical devices, automobiles, and household appliances, the distinction between product and service blurs. Courts have increasingly recognized that software can be subject to the same liability standards as physical components, especially when software defects lead to physical harm or property damage.

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Winter v. G.P. Putnam's Sons (1991)

In the landmark case of Winter v. G.P. Putnam's Sons, the United States Court of Appeals for the Ninth Circuit addressed the issue of liability for informational content. The plaintiffs became seriously ill after relying on a mushroom identification guide published by G.P. Putnam's Sons, which incorrectly classified a poisonous mushroom as edible. The court held that the publisher was not liable for the inaccuracies because imposing such liability would hinder the free flow of ideas and information protected by the First Amendment. The court distinguished between tangible products and ideas, suggesting that information in books does not constitute a "product" for the purposes of strict liability.

This case raises significant questions about liability for AI-generated content. If an AI system provides erroneous information that leads to harm, determining liability becomes complex. While the Winter case protected publishers to encourage the dissemination of ideas, AI systems often provide personalized and actionable recommendations, blurring the lines between mere information and professional advice.

Defective Software Litigation

As software failures have led to substantial damages, courts have begun to consider software defects under product liability law. Several cases illustrate this evolving legal landscape:

Mortensen v. Timberline Software Corp. (2000): In this case, a software malfunction led to significant financial losses for the plaintiff. The court held that the software company was not liable due to a limitation of liability clause in the software license agreement. However, the case highlighted the potential for software defects to cause real-world harm and the limitations of contractual protections.
Patco Construction Co. v. People's United Bank (2012): Although not a product liability case per se, this case involved the failure of a bank's security software, leading to fraudulent transactions from the plaintiff's account. The court found the bank's security procedures commercially unreasonable, holding the bank liable for the losses. This case underscores that entities deploying software have a duty to ensure its reliability and security.
Therac-25 Incidents (1985-1987): Although not resulting in a court case, the Therac-25 radiation therapy machine incidents are instructive. Software errors in the machine led to patient deaths and injuries. The incidents prompted significant scrutiny over software safety in medical devices, leading to stricter regulatory oversight by bodies like the FDA.

These cases demonstrate a growing recognition by courts that software defects can have tangible, harmful consequences, and that traditional legal frameworks can be applied to address these harms. As AI systems make autonomous decisions that can affect health, safety, and finances, the applicability of product liability law becomes even more pertinent.

Liabilities for AI Developers and Companies

Determining liability in the context of AI involves several layers, including the AI developers, the companies deploying the AI, and even the end-users. Generally, companies can be held liable under the doctrine of respondeat superior, which holds employers responsible for the actions of their employees performed within the scope of employment. This means that if an AI system causes harm due to a developer's negligence, the employing company may be held accountable.

However, individual developers might also face liability in cases of gross negligence or willful misconduct. For example, if a developer knowingly ignores safety protocols or intentionally introduces harmful code, they could be personally liable for resulting damages. Additionally, companies providing AI services may include indemnification clauses in their contracts to limit liability, but these clauses are not always enforceable, especially in cases involving consumer harm.

The deployment of AI in sensitive areas like healthcare, autonomous vehicles, and financial services further complicates liability issues. For instance, if an autonomous vehicle causes an accident due to a flaw in its decision-making algorithm, questions arise about whether the liability rests with the vehicle manufacturer, the software developer, or perhaps even the data providers who supplied the training data.

Implications for Generative AI

Generative AI systems, such as those producing text, images, or decisions based on data inputs, present unique challenges. Since these systems often operate with a degree of autonomy and unpredictability, attributing liability becomes more complex. If a generative AI system produces defamatory content, infringes on intellectual property rights, or provides harmful medical advice, identifying the responsible party is not straightforward.

Applying tort law requires a careful analysis of duty of care, breach, causation, and damages:

Duty of Care: AI developers and companies have a duty to create and maintain systems that operate safely and reliably.
Breach: Failure to adhere to industry standards, regulations, or reasonable practices could constitute a breach.
Causation: There must be a direct link between the breach and the harm caused.
Damages: The plaintiff must have suffered actual harm or loss as a result.

Courts may also consider the foreseeability of harm and whether the defendants took reasonable steps to prevent it. As AI technology evolves, so too will the standards for what is considered "reasonable" in the development and deployment of these systems.

Conclusion

The integration of generative AI into various aspects of society necessitates a re-examination of existing legal frameworks to address new forms of risk and liability. Adapting tort law to encompass software and AI technologies provides a practical approach to assigning responsibility and compensating victims without the immediate need for new legislation. By drawing on precedents from cases involving software defects and informational content, the legal system can navigate the complexities introduced by AI.

However, as AI systems become more sophisticated and autonomous, lawmakers and courts may need to develop new doctrines or modify existing ones to adequately address issues of liability. Collaborative efforts between legal experts, technologists, and policymakers will be essential in crafting regulations and guidelines that protect the public while fostering innovation.

In the interim, companies and developers should proactively assess the risks associated with their AI systems, implement robust testing and validation procedures, and stay informed about legal developments in this rapidly evolving field. By doing so, they can mitigate potential liabilities and contribute to the responsible advancement of AI technology.

Further read

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"Are you sure?": Unveiling the Quest for Human-Like Artificial Intelligence

Designers, creativity and AI

A LLM as a reflection of our inner most desires

Should AGI be a she, he, or a it?