The Legal Landscape of Artificial Intelligence (AI) Law
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While artificial intelligence (AI) technology has the potential to transform society, the legal issues it raises touch upon diverse areas of law. These areas include privacy and data security, commercial contracts, intellectual property, antitrust, employee benefits, and products liability.
AI is broadly defined as computer technology that can simulate human intelligence. Through algorithms, this software can aggregate data, detect patterns, optimize behaviors, and make future predictions. Some examples of AI applications include machine learning, natural language processing, artificial neural networks, machine perception, and motion manipulation.
Companies and organizations often use these technologies to perform functions more efficiently. They can either develop AI capabilities in-house or license AI technology from a third party.
Product Liability Law
AI has become a commonplace feature in many products and services. As a consequence, the potential for product liability claims has grown. AI’s ability to act autonomously raises novel legal questions with respect to personal injury and property damage claims.
Product liability law largely rests on state common and statutory law principles. Claims of negligence, breach of warranty, and strict liability constitute the traditional theories of product liability. These traditional theories of liability also apply in the AI context.
Negligence claims impose liability on a defendant for failure to satisfy a reasonable standard of care. This can stem from the fact that the product was negligently designed or contained inadequate warning labels.
Breach of warranty claims rest on the contractual relationship between the plaintiff and the defendant (the product seller). The plaintiff can allege a breach of an express warranty or an implied warranty. An implied warranty can come in two flavors: for the product’s merchantability or for the product’s fitness for a particular purpose.
Finally, strict liability is a standard under which a product manufacturer or seller is held responsible for personal injury or property damage regardless of the level of care exercised.
AI and a Bus Accident
The application of these product liability principles in the AI context was on display in the recent case Cruz v. Raymond Talmadge d/b/a Calvary Coach. The case featured an AI-driven product that has become a part of our everyday lives: a GPS device. In this case, the plaintiffs were injured when a bus struck an overpass. The plaintiffs’ lawsuit rested on claims of negligence, breach of warranty, and strict liability against the GPS device manufacturer. In particular, the plaintiffs pointed to what they said was a design defect in the GPS device. It routed the bus driver under an overpass. The plaintiffs asserted the GPS device should have been able to discern the overpass was too low for the bus. The parties eventually reached a settlement.
AI and Data Privacy
AI technology has created a host of issues around data privacy and automated decision-making. Data protection laws vary across the globe. In the United States, laws regulating the use of personal data in automated decision-making vary across state jurisdictions. In Europe, the EU General Data Protection Regulation, or GDPR, provides a uniform standard.
Despite the differences in legal regimes, a few underlying principles inform general thinking about these issues. These core principles include the Fairness Principle, the Purpose Specification Principle, and the Data Minimization Principle.
The Fairness Principle requires organizations to process personal information in a fair manner. This means implementing transparent measures to use personal information within individuals’ reasonable expectations and to mitigate the risk of discriminatory applications.
The Purpose Specification Principle requires organizations to collect personal information only for specific, defined purposes. This can be hard to implement in practice because organizations often cannot predict what algorithms will learn or the correlations algorithms will make with data sets. This can lead to the algorithms using data in unanticipated ways.
The Data Minimization Principle requires organizations to minimize the time they store the data and keep data usage limited to achieving the stated purposes of processing. This can be hard to implement in practice because AI technology tends to function best with larger data sets.
AI Technology and Intellectual Property Issues
AI technology touches upon issues of patent law, copyright law, and trade secrets. The technology is patentable through the designation of Class 706 (Data Processing: Artificial Intelligence) in the patent classification system of the US Patent and Trademark Office. The source code and visual elements of AI systems are protectable through copyright law. Finally, trade secret protection can be a useful form of IP protection for AI-related technology, and can apply to algorithms, source code, and AI training data sets.