Category: Machine Learning

Gray Advice

Artificial Intelligence, Current Volume, Data Analytics, Featured Articles, Health & Biotechnology, Machine Learning, Recent Publications, Science & Technology Leave a comment

By: Keith Porcaro Debates over economic protectionism or the technology flavor-of-the-month obscure a simple, urgent truth: people are going online to find help that they cannot get from legal and health professionals. They are being let down, by products with festering trust and quality issues, by regulators slow to apply consumer protection standards to harmful offerings, and by professionals loath to acknowledge changes to how help is delivered. The status quo cannot continue. Waves of capital and code are empowering ever more organizations to build digital products that blur the line between self-help and professional advice. For good or ill, “gray advice” is changing how ordinary people get help with legal issues and healthcare issues, and even how they perceive professionals. This Article begins the work of articulating what makes a high-quality digital advice product, and how regulators and professionals can engage with the reality of how people seek and find help today. Download Full Article (PDF) Cite: 25 Duke L. & Tech. Rev. 48

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Tribes and AI: Possibilities for Tribal Sovereignty

Artificial Intelligence, Current Volume, Featured Articles, Machine Learning, Recent Publications Leave a comment

By: Adam Crepelle Artificial Intelligence (AI) has permeated every facet of modern existence. Governments across the globe are exploring its applications and attempting to establish regulatory frameworks. Numerous scholars have proffered recommendations for governing AI at the local, national, and international levels. However, as is often the case, Indian tribes have been neglected in AI policy discussions. This oversight is significant because the 574 federally recognized tribes are sovereigns with their own judicial, education, and healthcare systems. Due to their relatively small populations and geographic isolation, tribes stand to benefit significantly from the services AI can perform. Moreover, tribes are uniquely well-suited to implement AI. This is the first law review article dedicated to exploring how AI can enhance tribal sovereignty. This article begins with a history of tribal sovereignty and then provides an overview of AI. Subsequent sections delve into the ways AI can augment tribal legal systems, healthcare, education, cultural preservation endeavors, economic development, and administrative capacity. By illuminating the intersection of AI and tribal sovereignty, this article seeks to foster a more inclusive discussion of AI. Download Full Article (PDF) Cite: 25 Duke L. & Tech. Rev. 1

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Unintentional Algorithmic Discrimination: How Artificial Intelligence Undermines Disparate Impact Jurisprudence

Data Analytics, Machine Learning, Recent Publications, Surveillance Tech Leave a comment

By: Vincent Calderon Artificial intelligence holds the capacity to revolutionize the economy by capturing efficiencies. These benefits, ostensibly, should pass down to consumers, thereby benefitting the general public. But the immense complexity of AI systems is bound to introduce legal hurdles for plaintiffs and frustrate our disparate impact jurisprudence. Specifically, demonstrating causation and proffering a less discriminatory alternative are herculean tasks for a plaintiff seeking to prove a disparate impact upon which legal relief may be granted. The courts have already begun to wrestle with these issues, primarily in the housing and employment sectors. With the rapid surge of AI systems, courts should expect further inquiry into how these programs interfere with our established antidiscrimination framework. This Note outlines how each step of a plaintiff’s successful disparate impact analysis is hindered by the opaque ways in which AI operates. This Note then proposes several policy reforms to mitigate these consequences. Download Full Article (PDF) Cite: 24 Duke L. & Tech. Rev. 28

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Can ChatGPT Keep a Secret? An Evaluation of the Applicability and Suitability of Trade Secrecy Protection for AI-Generated Inventions

Intellectual Property, Machine Learning, Recent Publications, Trade Secrecy 2 comments

By: Gina L. Campanelli The rising popularity of generative artificial intelligence has sparked questions around whether AI-generated inventions and works can be protected under current intellectual property regimes, and if so, how. Guidance from the U.S. Copyright Office and recent court cases shed some light on the applicability of copyright and patent protection to AI-generated products; namely “authors” and “inventors” are limited to natural persons. But further developments in copyright and patent law are still lagging behind generative-AI’s rapid growth. Trade secrecy emerges as the most viable path forward to protect AI-generated works and inventions because ownership of trade secrets is not limited to natural persons. But trade secrecy has its drawbacks too, primarily inadequate protection outside of misappropriation. Further, trade secrecy precludes disclosure, which hinders greater scientific development and progress. This Note examines the suitability and applicability of copyright, patent, and trade secret protection for AI-generated works and inventions and posits alternative protection schemes. Download Full Article (PDF) Cite: 24 Duke L. & Tech. Rev. 1

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Keeping Up With China: CFIUS and the Need to Secure Material Nonpublic Technical Knowledge of AI/ML

Alternative Intelligence, Machine Learning Leave a comment

By: Anthony Severin Artificial intelligence (AI) and machine learning (ML) technologies will shape societies by the values they are programmed to respect. In part because of anti-competitive Chinese practices such as forced transfers of intellectual property (IP), companies based in the U.S. have lost the ability to compete in several fields. To avoid losing competitiveness in AI/ML sectors, the Committee on Foreign Investment in the United States (CFIUS) should promulgate rules blocking Chinese investors from acquiring ownership interests in U.S. companies when that ownership would allow access to material nonpublic technical knowledge of AI/ML. Such a categorical blacklist approach will limit forced transfers of IP and increase the influence of American values on the development of AI/ML technology. Download Full Article (PDF) Cite: 19 Duke L. & Tech. Rev. 59

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