Lawfare Daily: Old Laws, New Tech: How Traditional Legal Doctrines Tackle AI
Dec 26, 2024
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Catherine Sharkey, a leading NYU law professor, joins Bryan Choi from Ohio State University and Kat Geddes from NYU and Cornell Tech to discuss the intersection of traditional legal doctrines and artificial intelligence. They explore the complexities of applying existing laws to AI liability and copyright challenges. The trio dives into evolving frameworks of product liability, highlighting the urgent need for adaptive legal approaches. They advocate for early regulation in emerging technologies to mitigate risks and protect society as AI continues to advance.
Traditional legal doctrines like tort and copyright struggle to adapt to the rapid advancements and ethical concerns posed by AI technologies.
The need for a balanced liability framework is emphasized, focusing on fostering innovation while ensuring accountability for AI developers' actions.
Differentiating between negligence and products liability is crucial, as it reveals the complexities in addressing issues arising from AI systems effectively.
Deep dives
Challenges of Traditional Legal Frameworks with AI
Traditional legal frameworks like tort and copyright face significant challenges when applied to AI technologies. Copyright law, while designed to protect creators, may not adequately address the ethical implications of AI, especially when generative models produce works that closely resemble existing art, literature, or imagery. Instances like the New York Times suing OpenAI highlight concerns over displacement of original content creators. Such legal actions raise fundamental questions about how existing laws can adapt to the rapid evolution of technology without sacrificing the original purpose of protecting creative expression.
The Impact of Liability on AI Development
The discussion surrounding liability for AI developers often centers on concerns similar to those in the medical field regarding defensive practices. Concerns exist that excessive liability might lead to AI developers adopting overly cautious or defensive strategies, potentially stifling innovation in the tech landscape. The question arises as to whether developers should be encouraged to take risks in creating new AI applications or constrained by fear of substantial litigation. Finding a balance between fostering innovation and ensuring accountability becomes increasingly critical as the technology advances.
Importance of Defining Harms in AI Applications
The panel highlighted the need to explicitly define the types of harm that may arise from AI applications, which can range from physical injuries related to AI-operated machines to economic losses and privacy violations. The example of cyber-physical systems, such as autonomous vehicles and drones, serves as a primary case for discussing product liability, as these have clear implications for physical safety. Additionally, concerns about creative harms in the realm of copyright law illustrate the wide spectrum of potential issues that need addressing. Effective liability frameworks must encompass both tangible and intangible harms to protect all stakeholder interests effectively.
Debating Negligence versus Products Liability in AI
The differences between negligence and products liability frameworks emerged as a prominent point of discussion among panelists. While negligence focuses on the behavior and care standards of developers, products liability looks at the safety and efficacy of the technology itself as a product. This distinction raises questions about which framework is more suitable for rectifying issues caused by AI systems, especially when they merge elements of both. The ongoing debate suggests that reflexive application of one singular approach may overlook the nuances presented by diverse AI scenarios, advocating for a more tailored application based on context.
The Role of Licensing and Regulation
Software licensing presents unique challenges in the application of liability, often allowing companies to include broad disclaimers that may seem enforceable within the confines of copyright law. These licenses provide developers with significant leeway to evade accountability for the products they create. However, conversations regarding liabilities linked to AI technologies underscore the necessity to question established legal frameworks and their flexibility. Ongoing discourse reinforces the idea that as AI evolves, the legal system must also reassess how licensing and regulation interact to ensure ethics and responsibility in development.
At a recent conference co-hosted by Lawfare and the Georgetown Institute for Law and Technology, Fordham law professor Chinny Sharma moderated a conversation on "Old Laws, New Tech: How Traditional Legal Doctrines Tackle AI,” between NYU law professor Catherine Sharkey, Ohio State University law professor Bryan Choi, and NYU and Cornell Tech postdoctoral fellow Kat Geddes.