Law Commons^™

Artificial Intelligence And Interspecific Law, Daniel J. Gervais, John J. Nay

Vanderbilt Law School Faculty Publications

Several experts have warned about artificial intelligence (AI) exceeding human capabilities, a “singularity” at which it might evolve beyond human control. Whether this will ever happen is a matter of conjecture. A legal singularity is afoot, however: For the first time, nonhuman entities that are not directed by humans may enter the legal system as a new “species” of legal subjects. This possibility of an “interspecific” legal system provides an opportunity to consider how AI might be built and governed. We argue that the legal system may be more ready for AI agents than many believe. Rather than attempt to …

Developing Ethics And Equity Principles, Terms And Engagement Tools, Ellen W. Clayton, Rachele Hendricks-Sturrup, Shilo Anders, Et Al.

Vanderbilt Law School Faculty Publications

Background:

Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research.

Objective:

AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities …

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. …