Law Commons^™

Human-Centered Design To Address Biases In Artificial Intelligence, Ellen W. Clayton, You Chen, Laurie L. Novak, Shilo Anders, Bradley Malin

Vanderbilt Law School Faculty Publications

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. …

Returning Integrated Genomic Risk And Clinical Recommendations: The Emerge Study, Ellen W. Clayton, Jodell E. Linder, Aimee Allworth, Sara T. Bland, 100 Others...

Vanderbilt Law School Faculty Publications

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. …

Ai Derivatives: The Application To The Derivative Work Right To Literary And Artistic Productions Of Ai Machines, Daniel J. Gervais

Vanderbilt Law School Faculty Publications

This Article predicts that there will be attempts to use courts to try to broaden the derivative work right in litigation either to prevent the use of, or claim protection for, literary and artistic productions made by Artificial Intelligence (AI) machines. This Article considers the normative valence of, and the (significant) doctrinal pitfalls associated with, such attempts. It also considers a possible legislative alternative, namely attempts to introduce a new sui generis right in AI productions. Finally, this Article explains how, whether such attempts succeed or not, the debate on rights (if any) in productions made by AI machines is …

A Compulsory Solution To The Machine Problem, Cole G. Merritt

Vanderbilt Law School Faculty Publications

Artificial Intelligence (AI) is already disrupting and will likely continue to disrupt many industries. Despite the role AI already plays, AI systems are becoming increasingly powerful. Ultimately, these systems may become a powerful tool that can lead to the discovery of important inventions or significantly reduce the time required to discover these inventions. Even now, AI systems are independently inventing. However, the resulting AI-generated inventions are unable to receive patent protection under current US patent law. This unpatentability may lead to inefficient results and ineffectively serves the goals of patent law.

To embrace the development and power of AI, Congress …

Fintech And International Financial Regulation, Yesha Yadav

Vanderbilt Law School Faculty Publications

This Article shows that fintech exacerbates the difficulties of standard setting in international financial regulation. Earlier work introduced the "Innovation Trilemma" (the Trilemma). When seeking to balance the goals of achieving market integrity and innovation through clear and simple rulemaking, regulators can-at best-achieve only two out of these three objectives. Fintech's unique characteristics- a reliance on automation and artificial intelligence, novel types of big data, as well as the use of disintermediating financial supply chains comprising a mix of traditional firms as well as technology specialists and newcomers-complicates the application of the Trilemma. Rulemaking struggles to achieve needed clarity where …

The Machine As Author, Daniel J. Gervais

Vanderbilt Law School Faculty Publications

Machines are increasingly good at emulating humans and laying siege to what has been a strictly human outpost: intellectual creativity.

At this juncture, we cannot know with certainty how high machines will reach on the creativity ladder when compared to, or measured against, their human counterparts, but we do know this. They are far enough already to force us to ask a genuinely hard and complex question, one that intellectual property (“IP”) scholars and courts will need to answer soon; namely, whether copyrights should be granted to productions made not by humans but by machines.

This Article’s specific objective is …

Exploring The Interfaces Between Big Data And Intellectual Property Law, Daniel J. Gervais

Vanderbilt Law School Faculty Publications

This article reviews the application of several IP rights (copyright, patent, sui generis database right, data exclusivity and trade secret) to Big Data. Beyond the protection of software used to collect and process Big Data corpora, copyright’s traditional role is challenged by the relatively unstructured nature of the non-relational (noSQL) databases typical of Big Data corpora. This also impacts the application of the EU sui generis right in databases. Misappropriation (tort-based) or anti-parasitic behaviour protection might apply, where available, to data generated by AI systems that has high but short-lived value. Copyright in material contained in Big Data corpora must …

Topic Modeling The President: Conventional And Computational Methods, J.B. Ruhl, John Nay, Jonathan Gilligan

Vanderbilt Law School Faculty Publications

Legal and policy scholars modeling direct actions into substantive topic classifications thus far have not employed computational methods. To compare the results of their conventional modeling methods with the computational method, we generated computational topic models of all direct actions over time periods other scholars have studied using conventional methods, and did the same for a case study of environmental-policy direct actions. Our computational model of all direct actions closely matched one of the two comprehensive empirical models developed using conventional methods. By contrast, our environmental-case-study model differed markedly from the only empirical topic model of environmental-policy direct actions using …